The Most Frequently Used English Phrasal Verbs in American and British English - A Multicorpus Examination

 

The Most Frequently Used English  Phrasal Verbs in American and British  English: A Multicorpus Examination  DILIN LIU University of Alaba University Alabama  ma  Tuscaloosa, Alabama, United States 

This study uses the Corpus of Contemporary American English and the British National Corpus as data and Biber, Johansson, Leech, Conrad, and Fin Finega egan’s n’s (19 (1999) 99) and Gar Gardne dnerr and Dav Davies ies’’ (20 (2007) 07) inf inform ormati ative ve stud st udie iess as a st star arti ting ng po poin intt an and d re refe fere renc nce. e. Th Thee st stud udyy of offe fers rs a cr cros osssEnglish variety and cross-register examination of the use of English phrasal verbs (PVs), one of the most difficult aspects of English for lear le arne ners rs of En Engl glis ish h as a fo fore reig ign n la lang ngua uage ge or En Engl glis ish h as a se seco cond nd language. The study first identified the frequency and usage patterns of  the most common PVs in the two corpora and then analyzed the results using usi ng sta statis tistic tical al pro proce cedur dures, es, the chi chi-sq -squar uaree and dis disper persio sion n tes tests, ts, to determine any significant cross-variety or -register differences. Besides  validating many of the findings of the two previous studies (although neithe nei therr was a cro crossss-Eng Englis lish h var variet ietyy exa examin minati ation) on),, the res result ultss of thi thiss stud st udyy pr prov ovid idee ne new, w, us usef eful ul in info form rmat atio ion n ab abou outt th thee us usee of PV PVs. s. In addi ad diti tion on,, th thee st stud udyy re resu sult lted ed in a co comp mpre rehe hens nsiv ivee li list st of th thee mo most  st  common PVs in American and British English, one that complements those offered by the two previous studies with more necessary items and more mo re de deta tail iled ed us usag agee in info form rmat atio ion. n. Th Thee st stud udyy al also so pr pres esen ents ts a cr cros osssregister list of the most frequent PVs, showing in which register(s) each of th thee PV PVss is pr prim imar aril ilyy us used ed.. Fi Fina nall lly, y, pe peda dago gogi gica call an and d re rese sear arch ch implications are discussed. doi: 10.5054/tq 10.5054/tq.2011.24770 .2011.247707  7 

ecause of their extremely high frequency in the English language and the great difficulty they present to language learners, phrasal  verbs (PVs) have long been a subject of interest and importance in English as a foreign language (EFL) or English as a second language (ESL) teaching and research, as evidenced by the many publications on the topic (Bolinger, 1971; Cordon & Kelly, 2002; Darwin & Gray, 1999; Gardnerr & Davies Gardne Davies,, 2007; Liao & Fukuya Fukuya,, 2004; McCarthy McCarthy & O’Del O’Dell, l, 2004; Side, 1990; Wyss, 2003). The unique challenge for teaching PVs is that, alth al thou ough gh PV PVss ar aree ub ubiq iqui uito tous us in th thee En Engl glis ish h la lang ngua uage ge,, EF EFL L or ES ESL L

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speake spea kers rs,, es espe peci cial ally ly th thos osee wi with th a lo lowe werr an and d in inte term rmed edia iate te le leve vell of  profi pr ofici cien ency cy,, co cons nsis iste tent ntly ly avo avoid id us usin ing g th them em (D (Dagu agutt & La Lauf ufer er,, 19 1985 85;; Hulstijn & Marchena, 1989; Laufer & Eliasson, 1993; Liao & Fukuya, 2004 20 04). ). Th Thee re reas ason onss fo forr th this is av avoi oida danc ncee ar aree ma many ny,, in incl clud udin ing g cr cros osssling li ngui uist stic ic di diffe ffere renc nces es an and d th thee com compl plex exit ityy of syn synta tact ctic ic an and d se sema mant ntic ic structures of PVs (Dagut & Laufer, 1985; Hulstijn & Marchena, 1989; Laufer & Eliasson, 1993). The enormous number of PVs in English also contributes the problem, because it makes learners overwhelmed, not knowingtowhich ones to learn. Thus identifying thefeel most useful PVs is paramount for language learning purposes. Although the answer to the ques qu esti tion on of wh whic ich h PV PVss ar aree us usef eful ul ma mayy va vary ry de depe pend ndin ing g on le lear arne ners rs’’ objectives and learning contexts, frequency is usually a good criterion for determining usefulness. This is because, in general, highly frequent  PVs are more useful than those with very low frequency. There have been be en tw two o co corp rpus us-b -bas ased ed fr freq eque uenc ncyy st stud udie iess of En Engl glis ish h PV PVss (B (Bib iber er,,  Johansson, Leech, Conrad, & Finegan, 1999; Gardner & Davies, 2007), and an d bo both th ha have ve pr prov ovid ided ed va valu luab able le in info form rmat atio ion n ab abou outt PV PVss an and d th thei eirr distribution patterns. Yet, there are important limitations in each of the two studies. It is important, however, to point out that the limitations are not to any oversight on the part offoci theand scholars did the studies but due simply the result of their specific spacewho constraints. Being a small section of a comprehensive book on English grammar, Biber et al.’s (1999) treatment of PVs is limited largely to a small set of  PVs (31 in total). Gardner and Davies’ (2007) work, though covering many more PVs than Biber et al.’s work, has three limitations of its own. First, their list of the most frequent PVs (a total of 100 items) contains only PVs made up of the top 20 PV-producing lexical verbs (e.g.,  come, go, ). In other words, the list does not include highly frequent  get,   and  take ). PVs formed by verbs outside the top 20 PV-producing ones (e.g.,  keep up  is not on the list because  keep   is not one of the top 20 PV-producing  verbs). As a result, their study, although offering new insights about PVs (e.g., a very small group account of lexicalofverbs makefrequent up a majori majority ty of PVs), does not provide a thorough the most PVs. Second, with the British National Corpus (BNC) as the data source, their study deals excl ex clus usiv ivel elyy wi with th Br Brit itis ish h En Engl glis ish. h. It re rema main inss an in inte tere rest stin ing g qu ques esti tion on  whether their findings are also true of any other major varieties of  English. In fact, in their conclusion, Gardner and Davies themselves explicitly called for the need to test the validity of their list ‘‘against other megacorpora’’ (p. 354). Third, limited by space, their study did not  render a cross-register examination of the frequently used PVs. Such cross cro ss-r -reg egis iste terr in infor forma mati tion on is is,, ho howe weve ver, r, ve very ry im impo port rtant ant for la lang ngua uage ge learning purposes, because it indicates the contexts where specific PVs are and are not typical. Gardner and Davies also explicitly recommended ‘‘a reanalysis of the [PV] lists across major registers (e.g., spoken versus 662

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 written English)’ English)’’’ (p. 354). In order to help fill in the aforementioned infor orm mati tion on gaps abo bou ut PVs, the pre ressent study aims to offer a  compa com parat rativ ivee in inve vest stig igati ation on of the mo most st fr freq eque uent ntly ly us used ed PV PVss be betw twee een n  American and British English and an examination of the usage information of these frequently used PVs across registers in American English.

DEFINITION OF PHRASAL VERB For any study of PVs, the definition of PV is often the first order of  business. Yet, what constitutes a PV and how to classify PVs have long been topics of debate. Many different theories have been proposed, and they differ largely over what syntactic and semantic features define a PV  and how such features should be used to classify PVs (Biber et al., 1999; Celce-Murci Celce -Murciaa & Larsen Larsen-Free -Freeman, man, 1999; Darwi Darwin n & Gray, 1999; Gardne Gardnerr & Davies, 2007; Quirk, Greenbaum, Leech, & Svartvik, 1985). However, many ma ny of th thee di diff ffer eren ence cess am amon ong g th thee th theo eori ries es ar aree qu quit itee mi minu nusc scul ule, e, especially from a language learner’s perspective. As Gardner and Davies (2007, p. 341) correctly note, ‘‘if even the linguists and grammarians struggle with nuances of PV definitions, of what instructional value could such su ch di dist stin inct ctio ions ns be fo forr th thee av aveera rage ge se seco cond nd la lang ngua uage ge lea earn rneer? r?’’’ Furthermore, because of the purposes of the present study, there is little need and room for a lengthy review of the various definitions that  have been proposed so far. This study had two main purposes: (1) to examine exami ne in the Corpus of Contem Contemporary porary American American English (COCA) the frequencies of the most common PVs and to compare the results with those reported in Biber et al. (1999) and Gardner and Davies (2007); and (2) to conduct a cross-register distribution analysis of the PVs in COCA and to compare the results with those of the study by Biber et al. In order to ensure a meaningful comparison between the findings of  this study and those of the other two, this study uses Gardner and Davies’ (2007) definition of VP: any two-part verb ‘‘consisting of a lexical verb (LV) proper . . . followed by an adverbial particle (tagged as AVP) that is eith ei ther er co cont ntig iguo uous us (a (adj djac acen ent) t) to th that at ve verb rb or no nonc ncon onti tigu guou ouss (i (i.e .e., ., separated by one or more intervening words)’’ (p. 341). The reason for using Gardner and Davies’ definition rather than Biber et al.’s is twof tw ofol old. d. Fi Firs rst, t, it is si simp mple ler, r, be beca caus usee it in invo volv lves es on only ly on onee sy synt ntac acti ticc criterion: ‘‘a verb plus an AVP.’’ In contrast, Biber et al.’s definition includes an additional semantic component: PVs must ‘‘have meanings beyond the separate meanings of the two parts [i.e., the verb and the  AVP]’’’ as in the case of ‘‘come on, shut up . . .’’ whereas verb   +   AVP  AVP]’ comb co mbin inat atio ions ns in wh whic ich h ‘‘t ‘the he ve verb rb an and d th thee ad adve verb rb ha have ve th thei eirr ow own n meanings’’ are ‘‘free combinations like  come back, come down . . .’’ (Biber PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

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et al., 1999, p. 404). The application of this semantic criterion is not  always straightforward straightforward and often involves some subjec subjective tive judgments. judgments. Of  cours cou rse, e, Ga Gard rdne nerr an and d Da Davie vies’ s’ sy synta ntact ctic ic cr crit iter erio ion n is not al alwa ways ys si simp mple le either, because whether a verb particle should be classified as an AVP, regular adverb, or preposition is sometimes open to debate, an issue I addr ad dres esss la late ter. r. The se seco cond nd re reas ason on fo forr usi sing ng Ga Gard rdne nerr an and d Da Davi vies es’’ definition is that, as is shown next, a majority of the most frequent  PVs examined in this study came from Gardner and Davies’ study.

METHOD Corpora Used  As mentioned earlier, the main corpus used for this study was COCA, a la larg rgee fr free ee on onli line ne co corp rpus us de deve velo lope ped d by Pr Prof ofes esso sorr Ma Mark rk Da Davi vies es of  Brig Br igham ham Yo Youn ung g Un Unive ivers rsit ity. y. Wh When en th this is st stud udyy wa wass co cond nduc ucte ted, d, CO COCA  CA  consisted of 386.89 million words via data gathered from 1990 to 2008, that is, an average of approximately 20 million words from each of the 19  years. The corpus contains five subcorpora: spoken, fiction, magazine, newspaper, and academic writing, with each subcorpus contributing an equal amount of data (4 million words per subcorpus per year). The corpus is also user friendly. Its search engine allows the user to perform, among other things, the search and comparison of ‘‘the frequency of   words, phrases and grammatical constructions’ constructions’’’ (Davies, 2008). Besides COCA, the 100.47-million-word BNC was also used both indirectly and directly: The frequency results of the 100 most common PVs in the BNC reported in Gardner and Davies’ study were compared with the PVs’ frequencies in COCA, and I queried the BNC directly through Davies’ (2005) BYU interface for the frequency information of the other PVs that are not on Gardener and Davies’ list of the 100 most frequent PVs. Furthermore, because the results of Biber et al.’s study were also used for compa com pari riso son n in th this is st stud udy, y, th thee co corp rpus us th they ey us used ed,, th thee 40 40-m -mil illi lionon-wo word rd Long Lo ngm man Sp Spok okeen an and d Wr Writ itte ten n En Eng gli lissh (LS LSW WE) co corp rpu us, was al alsso indirectly used in this study. To help the reader better understand the cross-corpora comparisons to be rendered in the Findings and Discussion section, some relevant  background information about the LSWE and the BNC is given here. Thee spo Th pok ken pa part rt of th thee LS LSWE WE co cons nsis ists ts pr prim imar ariily of fa face ce-t -too-fa face ce conversation (see Biber et al., 1999, p. 29–30). Similarly, a very large port po rtio ion n of th thee sp spok oken en su subc bcor orpu puss of th thee BN BNC C is co comp mpos osed ed of su such ch conv co nver ersa sati tion ons. s. In fa fact ct,, th thee Br Brit itis ish h En Engl glis ish h po port rtio ion n of th thee LS LSWE WE is included in the BNC. In contrast, the spoken part of COCA consists mostly of TV or radio broadcasting speech. 664

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Data Gathering and Data Reporting or Analysis Methods Querying for the frequency of a PV is a challenging task. One cannot  acccom ac ompl plis ish h th thee se sear arch ch by si simp mply ly en ente teri ring ng th thee le lexi xiccal ve verb rb le lemm mmaa of a PV in thee for th form m of [v [ver erb] b] pl plus us it itss pa part rtic icle le (e (e.g .g., ., ‘‘[ ‘[go go]] on on’’’) ’),, be beca caus usee no nott ev ever eryy on onee of the tokens generated by such a search is a phrasal verb. For example, the ‘‘[go] on’’ entry may yield non-PV tokens such as ‘‘We typically go on Mondays’’ is a preposition in theof  time phrase ‘‘on Mondays,’’where not an‘‘on’’ adverbial particle (AVP)   go . adverbial (The lemma search function helps generate the tokens of the various forms of the verb, e.g., go/goes/going/went/gone  for the lemma   go .) .) Thus, to ensure an accurate count cou nt of all the the toke tokens ns of a PV, soph sophis istic ticate ated d query query met method hodss are cal calle led d for. One such method is found in Gardner and Davies’ (2007) study. They  imported the entire tagged BNC data set into the Microsoft SQL server, a  relational data program that can help identify all the instances of PVs. This method was not used in this study, however, because COCA does not  make its entire tagged data set accessible to the public. Instead, this study  empl em ploy oyed ed ba basi sica call llyy a fo four ur-s -ste tep p pr proce ocedu dure re us usin ing g th thee ex exis isti ting ng se sear arch ch func fu ncti tion onss in CO COCA CA’s ’s in inte terf rfac ace. e. Th This is pr proc oced edur ure, e, th thou ough gh mo more re la labor bor intensive, be The fi firrstproved step wato s th the e sfunctional ear arch ch fo forr aand ll thefundamentally PV to tok kens of a laccurate. exica call lemma. This  was done by entering entering the verb verb lemma in the form of [verb] [verb] plus [RP*] [RP*] (RP is the search code for AVPs in COCA and the wildcard * stands for any   AVPs).  AVP s). For exa exampl mple, e, for all the PV toke tokens ns of the lex lexical ical verb lem lemma ma [go] [go],, ‘‘[go] [RP*]’’ is entered. The query will generate all the ‘‘go  plus   plus AVP’’ PV  tokens, includi including ng go on, go off,  and so forth. The second step was a search of  thee to th toke kens ns of tr tran ansi siti tive ve PV PVss use sed d wi with th th thei eirr AV AVPs Ps se sepa para rate ted d by on onee intervening word. This was carried out by entering for search ‘‘[verb] * [RP*], with the wildcard * between the verb and the AVP standing for any  intervening word. The third step was the search of the tokens of separable PVss wit PV with h tw two o in inter terve venin ning g wo words rds (e (e.g. .g.,,   look look th thee wo word rd up ). ). Th This is ta task sk wa wass performed * * [RP*]’’. wasmore done, however, for instancesbyofentering PVs with with‘‘[verb] their AVPs separatedNo separated bysearch three or interven intervening ing  words.  word s. This is because because PVs PVs so used are rare, and a search search for them will will yield yield ‘‘many false PVs’’ (Gardner & Davies, 2007, pp. 344–345). Furthermore, Gardner and Davies did not include such tokens, making it necessary to exclude them in this study to ensure a meaningful comparison. In steps 2 and 3, I read through the result lines to exclude any false tokens. All the aforementioned searches were performed with the cross-section comparison search function in COCA activated so that the search results included thee PV th PVs’ s’ fr freq eque uenc ncyy di disstr trib ibut utio ion n in eac ach h of th thee fi five ve re regi gist ster ers. s. Th Thee la last st st step ep wa wass the recording and tabulation tabulation of the query results, results, using using Excel spreadshee spreadsheets. ts. For each PV, the frequency results of its various forms in the five registers  weree ente  wer entered red,, and the subtotal and total frequenci frequencies es wer weree comp compute uted. d. PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

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 As far as the frequency counting counting or reporting reporting method method is concerned, concerned, raw  frequency numbers cannot be used for comparison purposes, because of  the large differences differences in size among the corpor corporaa used in the study. Instead, Instead, a  number  number of tokens per number of words  norming  norming method must be employed. For examining data in large corpora, researchers typically use the number of to toke ken ns pe perr mi mill llio ion n wo word rdss (P (PMW MWss) me meth thod od (c (cf. f. Bi Bibe ber, r, Co Conr nrad ad,, & Reppen, 1998; Biber et al., 1999; Liu, 2003, 2008; Moon, 1998). Furthermore, given method already of used in Biber al. (1999), it was adopted for that this this study for thewas reporting most of theet data. However, in the statistical analysis (i.e., the chi-square and the dispersion tests) of the results to determine whether there were significant differences among the PVs’ distributions, I used only raw observed frequencies, because normalized data are inappropriate for such statistical tests.

PVs Examined In order to render a comparison of the results of this study with those of Biber et al. (1999) and Gardner and Davies (2007), I queried COCA  for the frequency of all the PVs in their lists. There were a total of 31 PVs in Biber et al. Each had at least 40 tokens PMWs in at least one register of  the LSWE. Gardner and Davies’ list consists of 100 items made up of the top 20 PV-producing verbs. Twenty seven of the 31 in Biber et al.’s list  overlap, however, with those in Gardner and Davies’ list. In other words, only 4 of Biber et al’s 31 PVs are not in Gardner and Davies’ Davies’ list. Of these four, one is  go ahead.  It is not in Gardner and Davies’ list because ahead  is  is not tagged as an AVP in the BNC (or in COCA), but rather it is tagged as a regular adverb. The other three PVs not on Gardner and Davies’ list  are  shut up, stand up,   and  run out  because   because  run, shut,   and  stand  are   are not  among the top 20 PV-producing lexical verbs that Gardner and Davies identified. Because of the overlapping of 27 items, the total number of  PVs from Biber et al.’s and Gardner and Davies’ studies was 104, not 131. Besides Besid es searching searching these these 104 PVs in COCA, I also queried queried the COCA COCA and the BNC for the other most common PVs. To do so, I used the four most  recent comprehensive PV dictionaries as a search list guide:   Cambridge  (1997), ), wit with h ove overr 4,5 4,500 00 ent entrie ries; s; Intern Int ernati ation onal al Dic Dictio tiona nary ry of Phr Phrasa asall Ver Verbs  bs   (1997 (2000) 0),, wi with th ov over er 5, 5,00 000 0 PV PVs; s;   NTC’s  Longma Lon gman n Phr Phras asal al Ver Verbs bs Dic Dictio tiona nary  ry   (200  Dictionar  Dicti onaryy of Phra Phrasal sal Verb Verbss and Other Idiom Idiomatic atic Verb Verbal al Phra Phrases  ses   compiled by  Spears (1993), with 7,634 entries; and   Oxford Phrasal Verbs Dictionary for  Learne Lea rners rs of Eng Englis lish  h (2 (20 001 01), ), wit ith h ov oveer 6, 6,0 000 en entr trie ies. s. I se sear arcche hed d a to tota tall of 8, 8,8 847 PVs, 5,933 of which were from the dictionaries, whereas 2,914 were not. The latter were not searched intentionally but were the by-product of my  query method [verb] [RP*] which would automatically return all the PVs of the verb being queried, including those not in the dictionaries. For 666

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example, my queries [drive] [RP*] returned not only the intended PVs from the dictionaries, for example,  drive away/up/down/off,  but also those not li list sted ed in the di dict ctio iona nari ries es,, for ex exam ampl ple, e,   drive drive about/alon about/along/by/round  g/by/round . Cons Co nsid ider erin ing g th thee la larg rgee nu num mbe berr of PV PVss li list steed in ea each ch of th thee fo four ur di dict ctio iona nari ries es,, one may wonder why only 5,933 PVs were queried. The reasons were (1) many of the entries in the dictionaries overlap, and (2) the dictionaries include  verb   +  preposition  structures   structures (e.g., abide  by  and   and accede  to ) that are not considered PVs relative to the definition used in this study.  According to Gardner and Davies’ (2007) search, there are a total of  12,508 PV lemmas in the BNC. This means that my query of 8,847 left  3,661 PVs unsearched. This should not, however, be a concern for the following reasons. First, the purpose of my study was to identify the most  frequently used PVs, and the criterion for inclusion in my list was 10 tokens PMWs. As the immediately following discussion shows, only 152 out of the 8,847 made the list. Most PVs simply do not have the required frequ fre quenc ency. y. Sec Second ond,, my sea search rch cov covere ered d all the the lexic lexical al ver verb b le lemm mmas as that that had a total of 1,000 tokens in the BNC or 3,869 in COCA, because this was the minimum number that would give the verbs the potential for yielding the required number of PV tokens to make the most common PV list. Finally, because of tagging errors, not all of the 12,508 PV lemmas are PVs.  As already stated, the criterion for a PV to make the most frequently  used list in this study was 10 tokens PMWs in either COCA or the BNC. The rationale for using this criterion was threefold. First, 73 (70%) of  the 104 PVs on the Biber et al. and Gardner and Davies’ combined list  each have 10 tokens or more PMWs; only 31 on Gardner and Davies’ list  each show a frequency fewer than 10 PMWs. Second, in order to be truly  meaningful, a list of the most frequently used PVs should not be too long. Third, as Gardner and Davies (2007) reported, the 100 frequently  used PVs they identified already ‘‘account for more than half (51.4%) of  alll the PV occ al occur urre renc nces es in th thee BN BNC’ C’’’ (p (p.. 35 351) 1)..1 Usi Using ng thi thiss ten ten-to -token ken PMWs criterion, my search identified 48 additional most frequently-used PVs. The search results also showed that these 48 PVs and the four from 1

It is necessary to note that there is an error in the frequency number of a PV in Gardner and Davies’ data that has an implication for the total numbers they reported. In their 100 most common PV list,  carry out  is  is ranked as the 2nd most frequent PV, boasting a frequency  of 10,798. This frequency number is unusually high and incorrect, based on my search and consultation with Mark Davies, one of the authors of the Gardner and Davies article. The correct number is 4,180, which means that their reported frequency of this PV is 6,618 tokens over the actual frequency. This should also have resulted in an inflation of the total PV occurrences in the BNC by 6,618. Thus, with the 6,618 removed from both the token numbers of the 100 PVs (266,926   2  6,618) and the total token numbers of all the PV  occurrences in the BNC (518,283   2  6,618), the tokens of the 100 PVs (260,168) should account for 50.78%, instead of the 51.7%, of the total PV tokens (512,305) in the BNC. These adjusted correct numbers are used in the discussion in the remainder of the article.  Also, in the appendix, the frequency number and order of   carry out  in the BNC list is adjusted accordingly (from 2nd to 24th).

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Biber et al. that are not on Gardner and Davies’ list together account for another 12.17% of all the PV occurrences in the BNC. This means that  thee 15 th 152 2 mo most st fr freequ quen entl tlyy-us useed PV PVss co comp mpil ileed in th this is stu tudy dy,, wh whiile comprising only 1.2% of the total 12,508 PV lemmas in the BNC, cover 62.95% of all the total 512,305 PV occurrences. This helps demonstrate the representativeness and hence the usefulness of these most-frequently  used us ed PV PVs. s. Of co cour urse se,, th ther eree ar aree se seve vera rall li limi mita tati tion onss th that at sh shou ould ld be considered when using this list for learning/teaching purposes, such as the fact that it is a lemmatized list and that many of the PVs have multimeanings, two very important issues I will address in the next section.

FINDINGS AND DISCUSSION Most Frequently Used Phrasal Verbs: American English Versus British English This st This stud udyy has un unco cove vere red d th thee fre frequ quen ency cy in info form rmat atio ion n of 15 152 2 PV PVs, s, including the 100 from the Gardner and Davies list, the four from Biber et al. that are not in Gardner and Davies’ list, 2 and the 48 additional most frequent PVs this study has identified. The frequency information is reported in a table format in the appendix, with the PVs listed in order of their frequency in COCA. To allow for an easy comparison of the PVs’ frequency in COCA with their frequency in the BNC, their frequency  and rank order information in the BNC is also provided (in the second and third columns from the right). It is necessary to note that the total number of PVs in the appendix is 150, not 152, because I combined the PVs in each of the following two related pairs that were reported as individual PVs in Gardner and Davies’ study (2007):  look around  and  and  look  round ;  turn around   and  turn round . Gardner and Davies also have  come   and  go round  on   on their list but not  come  and  go around , given round  and   come around  and that the latter forms are the dominant uses in American English, I have included and combined them with the former in this study. The reason for combining the two forms in each pair is that they are synonymous and that they represent mainly a usage variation between American and British English, an issue that is discussed later. Before proceeding to a detailed comparison of the PVs’ frequency and usage patterns in the two corpora, I briefly discuss how some of the results of this study support Biber et al.’s (1999) and Gardner and Davies’ (2007) findings about an interesting aspect of PVs: A relatively small number of  lexical verbs and AVPs form the majority of the PVs in English. Biber et al. 2

One of them is  go ahead . Even though it is not tagged as a PV, as mentioned earlier, I have included it not only because Biber et al. (1999) did but also because I believe  ahead   is actually an AVP for the verb   go , making the phrase a true PV.

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identified eight verbs and six adverbs as the most productive in forming PVs. PV s. Ga Gard rdne nerr an and d Da Davi vies es id iden enti tifi fied ed th thee top 20 PV PV-p -pro rodu duci cing ng ve verbs rbs an and d th thee four most ‘‘prolific’ ‘‘prolific’’’ AVPs that help form for more than half (53.7%) of all the PVs in the BNC (2007, p. 347). 3 The same pattern is found in the lexical verbs and the AVPs in the 52 additional most frequent PVs (48 identified in this study and 4 from Biber et al.). For example,  out  and  and  up  are each the AVPs in 19 of the 52 PVs, that is, they combine for the AVPS of 38 (73.08%) of the 52 PVs. in these is important to first recall thatConcerning all of themthe areverbs outside the 52 topPVs, 20 it PVproducing lexical verbs. Yet even these less productive verbs show some concentrated use in PVs. One of them (hang ) appears in three of the 52 PVs, and five ( fill, keep, pull, show, stand ) each appear in two. To co comp mpar aree the PV PVs’ s’ fre frequ quen ency cy di dist stri ribu buti tion on pa patt tter erns ns in the tw two o corpora, it is necessary to note that the data of the two corpora do not  come from the same time period. Although the BNC covers the 1980s to 1993, COCA extends from 1990 to the present, that is, COCA starts basically where the BNC ends. This difference in time periods could be resp re spon onsi sibl blee fo forr so some me of th thee PV us usag agee va vari riat atio ions ns be betw twee een n th thee tw two o 4 corpora, which is discussed later. To compare the general frequency  patterns of the PVs in the two and to determine whether there is any significant difference callscorpora for a chi-square test of the raw observed frequencies. Given the large difference in size between the two corpora, a one-way chi-square test of the observed frequencies of the PVs from the two corpora would not make sense. To account for the effect of the difference in corpus size, I opted for a two-way chi-square test with the total observed frequencies of the 150 PVs measured against the total number of words of their respective corpora minus the total number of  tokens of the 150 PVs. In this way, the problem of difference in corpus size was controlled, allowing the chi-square test to determine whether the relative frequency of the PVs was statistically equal in both corpora. The results are reported in Table 1 where I also include at the bottom the PVs’ frequencies PMWs in the two corpora for easier comparison.  A close look at the test results indicates that, although there is a  significant difference between the frequencies of the PVs in the two 3

 Although most of the top PV-producing verbs and AVPs identified by Biber et al. (1999) overlap with Gardner and Davies’ (2007), the rank orders of the items between the two lists differ. For example, whereas  take  and  and  get  are  are first and second on the Biber et al. list,  go  and  and come  are the first two on Gardner and Davies’ list (also my COCA list). The difference appears to have resulted from the different definitions of PV used. As mentioned earlier, Biber Bib er et al. al.’s ’s def definit inition ion invo involve lvess a sem semant antic ic crit criteri erion, on, whic which h exc exclud ludes es ver verb b   +   adverb combinations where verb and AVP hold separate instead of combined meanings. Thus Biber et al. excluded many of the highly frequent PVs formed by  come    come  and  and  go  (e.g.,  (e.g.,  go back  and  come in ) listed in Gardner and Davies (2007). 4 I owe this idea to an anonymous reviewer, who suggested that the increased use of certain PVs over the past 20 years in COCA may explain their higher frequencies in COCA than in the BNC. PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

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TABLE 1 Comparison of the Most Common PVs’ Overall Frequency Patterns in COCA and the BNC

Total observed frequency of the 150 PVs Total number of   words minus the 150 PVs’ total tokens Frequency PMWs of the 150 PVs

df     Chi-square (x2)   P    Cramer’s V 

COCA

BNC

1,424,836 (+2.7%)*

322,517 (210.5%)*

1

4,988.65

0.0001

0.0032

385,465,164

100,147,483

1

4,988.65

0.0001

0.0032

3,682.79

3,210.09

 

Note.  COCA  5 Corpus of Contemporary American English; BNC 5 British National Corpus; PVs 5 phrasal verbs. *Percentage that the observed frequency deviated deviated from the expected frequency.

corpora, the difference is actually minuscule, as evidenced by the very  small  effect size,  a Cramer’s V of only 0.0032, and also by the percentages of de devi viati ations ons (P (PDs Ds)) of th thee ob obse serv rved ed fre frequ quen enci cies es fr from om th thee ex expe pect cted ed frequencies, with the frequency in COCA being merely 2.7% higher than expected and the frequency in the BNC being only 10.5% lower than expected. expec ted. The effec effectt size is extrem extremely ely important for statis statistical tical analysis in corpus research, because, as Gries (2010, p. 286) explained, ‘‘the large sam sa mpl plee si size zess th that at ma many ny co cont ntem empo pora rary ry co corp rpor oraa pr prov oviide ba basi sica callly  guarantee that even minuscule effects will be highly significant.’’ Thus the significant difference shown by the chi-square test is very likely the result of the large size of the two corpora. Furthermore, a comparison of  all the individual PVs’ frequency rank order in COCA against their rank orde or derr in th thee BNC (t (the he re resu sult ltss re repo port rted ed in th thee la last st co colu lum mn of th thee appe ap pend ndix ix)) in indi dica cate tess tha thatt th thee PV PVs’ s’ fre frequ quen ency cy ran rank k or orde ders rs in th thee tw two o corpora are fairly similar. For example, for each of the following five PVs, its frequency orders in both corpora are iden identical: tical: go on  1st,  1st,  come in  14th,  14th,  19th,  44th, and   94th. (Incidentally,   is get back one in Biber  turn alsoback  the most bring frequent et down  al.’s study.) Eight out of  go theon  top 10 PVs in the COCA list also make the top 10 in the BNC list. Forty-six (30.67%) of the 150 PVs show only a single digit difference between their rank orders in the two corpora (e.g.,  pick up  ranks   ranks 2nd in COCA  5 and 3rd in the BNC, a rank difference of 1). Thirty-seven (24.67%) reco re cord rd a ra rank nk or ord der di diff ffeere renc ncee be betw tweeen 10 an and d 19 19.. Ho Howe weve ver, r, 67 (44.67%) display a rank order difference of 20 or above, an issue I return to later. 5

This rank difference number can be interpreted to mean, depending on one’s perspective, either that the frequency of  pick   pick up  in  in COCA is one rank higher (i.e.,  + 1) than its frequency  in the BNC, or its frequency in the BNC is one rank lower ( 21) than its frequency in COCA. To make the reporting of this rank order comparison simpler, no   +/– sign is used.

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Given the different time periods the BNC and COCA each cover, the absence of a truly large difference in PV use between the two corpora may  suggest that PV use has remained fairly stable. This fact may in turn imply  that the list of the most frequently used PVs produced in this study may   withstand the test of time. In that case, What about the differenc differences es in PV  uses found between the two corpora, especially the rank disparity of 20 or more found in 67 of the PVs? What might be the cause(s) for the differences? To answer these questions, we should first understand how  and to what extent the frequencies and uses of these PVs in the two corpora differ. A close examination reveals that, although the differences of their rank orders between the two corpora offer some interesting inform inf ormati ation, on, the di diffe fferen rence ce bet betwe ween en a PV’ PV’ss fre freque quenci ncies es (nu (numbe mbers rs of  tokens) in the two corpora is a much more informative indicator. For example, the difference between the rank orders of  come   come up  in   in the two corpora is only five (4th in COCA and 9th in the BNC), but its frequency  difference in the two corpora is 55.45 PMWs. In contrast,  set off   has a rank order difference of 49 but a frequency difference of only 6.81 PMWs. Therefore, I decided to use frequency as the main criterion to examine the individual PVs’ distribution differences in the two corpora. Specifically, I tested for any significant difference between the raw  frequencies of those PVs whose frequencies in the two corpora varied by  10 or more PMWs. There were a total of 39 such PVs. Given that the two corpora differ tremendously in size, I conducted a two-way chi-square test te st em empl ploy oyin ing g ex exac actl tlyy th thee sa same me me meth thod od us used ed fo forr te test stin ing g th thee to tota tall frequency difference of the 150 PVs in the two corpora reported earlier in Table 1. Because of the large size of the corpora, the chi-square results for the 39 PVs were all significant, but their Cramer’s Vs were very small, ranging from 0.0006 to 0.0019. In order to have a shorter and more focused list of PVs which show a truly noticeable difference in their distributions between American and British English, I excluded from the list those PVs with Cramer’s Vs lower than 0.001. This resulted in a list of  30 PVs. Twenty are significantly more common in American English: check out, come out, come up, figure out, get out, go ahead, grow up, hang out, hold up, lay out, pick up, pull out, show up, shut down, take off, end up, turn    (cf. the appendix table for their out, take on, turn a/round,   and  wake up  (cf. rank orders or frequencies in the two corpora). Ten appear significantly  more frequently in British English:  build up, carry on, fill in, get on, set out, set up, sort out, take over, take up,   and  turn up . Although the reasons for some of the PVs’ prominent use in one of the two English varieties are difficult to determine, the causes for some can be attributed to either usage differences between the two varieties of English or the increase of  use in American English, for, as mentioned earlier, COCA starts where the BNC ends in terms of the time periods covered.

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Regarding usage differences, an examination of some of the tokens of  the PVs confirms the following information indicated by some of the PV  dictionaries. The significantly larger number of tokens of  fill in   in the BNC appears related to the fact that British English typically uses  fill in  in  in ‘‘ fill  or  fill something in  a   a form/document,’’ whereas American English  fill in  or generally uses  fill out  in   in such cases. The quadrupled use of  check   check out   in COCA CO CA co comp mpar ared ed to th that at in th thee BN BNC C is th thee re resu sult lt of th thee mul ulti tip ple functions or meanings of the PV in American English that are not found in British English, such as its meaning ‘‘paying for things’’ at a store and ‘‘borrowing items from a library.’’ Furthermore, the far less frequent use of  shut  in the BNC is mostly due to the fact that, in British English,   shut down  in shut up   is often used to express the meaning of ‘‘closing a business temp te mpora orari rily ly,’ ,’’’ a me mean anin ing g al almo most st al alwa ways ys ex expr pres esse sed d by   shut shut do down  wn   in  American English. This fact also helps explain the lower frequency and rank order of  shut   in COCA.   shut up  in  Another  Anot her noti noticeab ceable le use diff differenc erencee bet between ween Amer American ican and Brit British ish English, as mentioned earlier, relates to the use of  around/round    around/round  in   in the PVs such as  come around/round, go around/round, look around/round,  an  and d turn around/round . The distribution of   around/round   in these PVs in the two corpora is reported in Table 2. The results demonstrate that, although it is true Americans prefer  around  and   and British speakers favor round , Americans’ preference for  around  over   over  round  is   is much stronger than the British preference for  round  over   over  around . The American use of   around   is more than 90% of the time in each of the four PVs,  whereas  wher eas the Brit British ish use of  round  is   is in general much less than 90% of  the time. Concerning frequency differences likely caused by the increased use of certain PVs in American English, a query of COCA indicated that  check   check    each show a noticeable increase in out, hang out, show up , and  come up  each number of tokens from 1990–1994 to 2005–2009.  Check out  increased   increased by  102%,   hang out   by 52%,   show up   by 25%, and   come up   by 23%. Such substantial increases of the PVs in COCA may help explain their higher frequencies in COCA than in the BNC. Yet, because we do not have the British English data after the early 1990s, we cannot be certain whether the same increa increases ses would have also occurred in Britis British h Engli English. sh. In short, the analysis of the PVs’ frequency patterns indicates that, although their general distribution patterns are very similar in both corpora, there are some differences concerning some specific PVs because of (1) usage differences between American and British English and/or (2) increased use in American English. Knowledge of these differences is useful to English language educators when deciding which PVs should be taught  and learned in which English variety.

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TABLE 2 Distribution of the PV Particles  Around/Round  in   in COCA and the BNC

PV

COCA

 Verb

AVP

come

around   round  around  

 

round  around   round  around   round 

        

go look turn Note.  AVP

5

Tokens PMWs    

BNC Percentage

Tokens PMWs

Percentage

6.11 0.39 9.37

94 6 93

1.40 11.02 4.36

11 89 24

20 0..7 50 4 0.21 26.82 0.55

97 9 1 98 2

13 60 7..7 6 6.91 4.21 11.41

7 56 3 47 27 73

 adverbial particle; PMWs

5

per million words.

Cross-Register Differences in the Use of PVs To de dete term rmin inee wh whet ethe herr th ther eree is a si sign gnif ific ican antt di diff ffer eren ence ce in th thee ove overal ralll ra raw  w  frequ fre quen ency cy di dist stri ribu buti tions ons of th thee 15 150 0 PV PVss am amon ong g th thee fi five ve re regi gist ster erss in CO COCA CA,, I conducted a one-way chi-square test and a dispersion/adjusted frequency  testt us tes using ing Gri Gries es’’ (2 (2008 008b) b) Di Dispe spersi rsions ons2 2 pro progra gram. m. Thi Thiss di dispe spersi rsion on tes test  t   yields, in addition to a series of adjusted frequencie frequencies, s, a deviation of  proportion (DP) score, which theoretically can range from 0 to 1, but  sometimes the number of parts of the corpus and other factors may  prevent it from reaching the maximal value of 1. To address this problem, thee te th test st al alsso gi givves a no norrmali lizzed DP sc sco ore re,, sh sho own as DPnorm, which is abl blee to display the maximal value. The values of DP near 0 suggest that the freequ fr quen enci cies es of a li lin ngu guiist stiic it iteem ar aree di disstr triibu bute ted d in pr prop opor orti tion on to th thee si size zess of  the corpus registers or parts, whereas high values, especially those near 1, signify that the frequencies of the linguistic item are distributed very  unevenly across the registers. An adjusted frequency is a downwardly  adjusted total frequency in proportion to the degree of the unevenness of  the distribution of the linguistic item. The results of both the chi-square and th and thee di disp sper erssio ion n te tessts ar aree re repo port rteed in Ta Tabl blee 3. Be Bessid idees th thee ra raw  w  frequencies of the PVs, I have also reported the frequencies PMWs so the results can be compared with those of the Biber et al. (1999) study. The result of the chi-square test is very significant, with   p   ,  0.0001, but the devi de viat atio ions ns of th thee PV PVss ac acro ross ss th thee re regi gist ster erss ar aree no nott pa parti rticu cula larl rlyy hi high gh accord acc ordin ing g to Gr Grie ies’ s’ss DP (0 (0.2 .214 14;; 0. 0.26 268 8norm). It is im impo port rtan antt to no note te,, howe ho weve ver, r, th that at th thee sp spec ecif ific ic pe perc rceent ntag agee de devi viat atio ions ns of th thee ob obsser erve ved d frequencies of the PVs from the expected are fairly high: Whereas the observed frequencies in the spoken and fiction registers are, respectively, 44.34% and 66.12% higher than the expected, those in the magazine, news ne wspa pape per, r, an and d aca acade demi micc re regi gist ster erss ar aree 18 18.3 .36% 6%,, 21 21.0 .02% 2%,, an and d 66 66.8 .86% 6% lo lowe werr than the expected, respectively.

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   t    s    e     T    n    o     i    s    r    e    p    s     i     D    s     ’    s    e     i    r     G     d    n    a    t    s    e     T    e    r    a    u    q     S       i     h     C    y    a     W      e    n     O    a     f    o    s    t     l    u    s    e     R    e     h    t     d    n    a    s    r    e    t    s     i    g    e     R    e     h    t    s    s    o    r    c     A    n    o     i    t    u     b     i    r    t    s     i     D    s     V     P    t    n    e    u     3    q    e    r     E    F     L    t     B    s     A    o     T    M

674

   s     ’    s    e    m    m     i    r    r    r    o     P    o    n    n     G     D     8    s     6     8     6     ’    y    s     2     2     b  .  .    e     i    r     0     0     d     G     4     4    e     1     1    c     2     2  .  .    u     0     0     d    o    r     1     1    p     0     0     0     p     0    y     0     0    c  .  .     0     0    n    e      x     8     8    u    q     8    e    e  .  .    r    r     8     f     5     5    a     4     4    u     4     d    q  ,     4  ,    s    e       4    t     i     2     4    s     h     3     2     3    u     C     j      d    a     f     4     4     d    s     ’    n    e    r     *    g     *     )     6    n     4     9     3    e     9     7    s     7     8  .     l     8  ,  .    o     4    a     3  ,     4    t     8     R     9     2    o     6     8     3    6     *  ,     T     3     4     *  ,     3     3  ,     1    1  .    y     (    c    n    e     *    u     )    c     8    q     i     0     1    6    3    e    m     2    e  .     4  ,    8     6  .    3     9  .     f    r     6     d     6     2     7     4     d  ,    a     9    e     1    2    c    t     (     A    c    e    p    x     *    e     )    r     9    e     3     7    %    e     6    p     7     2     h  .     0    a     3    t  .     5  ,    0     8  .    p     6    s     1    4     7     2    m    w  ,     2    2    9    o    e     2    2    r     (     f     N     d    e    t     *     )    a     i    e     0    v     9     %     7    n     6    e     3     6     i  .     6     2     d    z  .  ,     3     8  .    a     0     4    8    2    y    g    c    a     8     4  ,     2    1    0    n     3    2     M    e     (    u    q     *    8     )     f    e    r     0     %     2    n     8     8     d  .    o     8  .     7  ,    2    e     1     i     5  .    t     4     9    v     0    r    c     7     4    6     i     0    e     6  ,     4     F    s       +     6     b     (    o     *    e     )     h     6    t     5     %    n     2     2     5     4    e  .     8     3    e  .  ,    3     k     8  .    8    g     1    o     7     1     4    a     1     2    t    p     4  ,     4     S    n       +     5    e  .     (    t    c    s    r    e   e    y    t    c    p    2    n    e    y    e   s    n    u     h    c     )    q    n    o     i     T    n    e    e    s     *    r    r    s    u   s    o     f     i    e     l     V  .     l     W    q    e    p     i    e     P    w    t    s    z    m    a    f    e    r    M    o    i     i     S    (     R   o    F    P     N    D         2

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It is thus clear from the test results that the PVs are much more common in fiction and spoken English than in magazines, newspapers, and, especially, academic writing. The results support the conclusion of  Biber et al. (1999, p. 408) on the issue: ‘‘Overall, phrasal verbs are used most commonly in fiction and conversation; they are rare in academic pros pr ose. e. In fi fict ction ion an and d co conv nver ersa sati tion on,, ph phra rasa sall ve verb rbss occ occur ur al almo most st 2, 2,00 000 0 times per million words.’’ The only difference between the finding of  Biber et al. and my finding is the rate of occurrence. Although the PV  frequency in fiction and conversation in their study is ‘‘almost 2,000 per million words,’’ the rates in the two registers found in this study are almost three times that. The reason for this large difference between their number and mine appears, again, to be the narrower definition of  PV used in their study, an issue explained earlier (Footnote 3). One can attribute the difference to this reason quite confidently, because the frequency numbers in my study and in Gardner and Davies’ (2007) study  are quite comparable, and our two studies used the same definition. In Gardner and Davies’ study, the frequency of the top 100 PVs in the BNC is 278,780 or 2,788 PMWs (p. 349), a number that would have been even higher if it had included those of the 50 PVs included in my study. Furthermore, given that the 2,788 PMWs frequency is the average that  included the much lower frequencies in the newspaper and academic  writing registers, one can certainly expect the numbers in their spoken and fiction registers to go much higher than 2,788 PMWs.  Although the overall cross-register analysis provides information abo ab out the PVs’ general distribution pat attterns, it doe oess not offe ferr info in form rmat atio ion n ab abou outt th thee be beha havi vior oral al pa patt tter erns ns of th thee in indi divi vidu dual al PV PVs, s, especially those that actually occur more often in the registers other than in fiction or speech. Such information is very useful for language learning. Therefore, I conducted an analysis of each individual PV’s raw  observed frequencies across the five registers, using both a one-way chisqua sq uare re te test st and Gr Grie ies’ s’ (2 (200 008b 8b)) Di Disp sper ersi sion ons2 s2 te test st.. Al Altho thoug ugh h th thee ch chiisquare test reveals that every one of the 150 PVs showed a significant  difference ( p   ,  0.001) in its frequencies among the five registers, the dispersion test shows their DPnorm values vary substantially, ranging from 0.045 (in the case of  make ).6 This means  make up ) to 0.74 (in the case of  look  look up ). that th at,, of th thee 150 PV PVs, s,   make dist stri ribut buted ed mo most st eve evenl nlyy acr acros osss th thee make up   is di registers, whereas  look up  is   is distributed most unevenly. Based on their DPs, I divided the PVs into three groups: (1) fairly evenly distributed,  with a DPnorm   belo below w 0. 0.25 25 (6 (65 5 of th them em or 43 43.3 .33% 3%), ), (2 (2)) no nott ev even enly  ly  distributed, with a PDnorm  between 0.25 and 0.499 (68 or 45.33%), and (3) very unevenly distributed, with a PDnorm   of 0.5 or above (17 or 11.3 11 .33% 3%). ). Th Thee la latte tterr tw two o ty type pess com combi bine ne fo forr 85 (5 (56.6 6.67% 7%). ). Eac Each h PV PV’s ’s 6

DPnorm, instead of DP, is opted for because of its ability to show the maximal value. PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

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dispersio disper sion n cla classi ssific ficati ation on is sho shown wn in the app append endix ix wit with h a sup supers erscri cript  pt  number 1, 2, or 3 after the PV. Clas Cl assi sify fyin ing g PV PVss by di disp sper ersi sion on pa patt tter ern n is ve very ry us usef eful ul fo forr la lang ngua uage ge learning because, as Greis (2008a) showed in his literature review, the dist di stri ribu buti tion onal al ra rang ngee of le lexi xica call it item emss ha hass a gr grea eatt im impa pact ct on se seco cond nd language (L2) learners’ processing and learning of them. Items that  boast a wider and more even distributional range are processed faster than those that have a narrow one, and hence they should be of higher priority for L2 learners. However, this does not mean one can overlook those unevenly distributed PVs in language learning. In fact, the latter PVs occur mostly in one or two registers, and, as such, they are actually   very important for English for specific purposes (ESP) learners, who, because of their specific purpose of study, must focus on the register(s) in which these PVs appear most frequently. For example,  carry out  is   is an unevenly distributed PV because of its very high frequency in academic  writing. For students studying academic English, it should thus be very  high on their list of PVs to be learned. Before examining in detail the noti no tice ceab able le di dist stri ribu buti tion on pa patt tter erns ns of th thee 85 si sign gnif ific ican antl tlyy un unev even enly  ly  distributed PVs, it is important again to note that the PVs reported here are lemmatized and many of them are polysemous, just as most PVs aree in ge ar gene nera ral. l. Th Thee di dist stri ribu buti tion on of th thee di diff ffeere rent nt mea eani ning ngss of a  polysemous PV may vary significantly across registers. For exa exampl mple, e,   make up   can mean, among other things, compose or constitute (e.g., ‘‘Women  make up  22   22 percent of the rural labor force in Nicaragua . . .’’); decide, when used in ‘‘make up  one’s   one’s mind’’ (‘‘Secretary  Powel Pow elll can make up hi hiss ow own n mi mind nd’’’) ’);; co comp mpen ensa sate te (f (for or)) (e (e.g. .g.,, ‘‘T ‘The he ki kids ds make   for their lack in experience with enthusiasm’’); and fabricate (‘‘Melanie up  for  that story’’).7 I examined the meanings of the first 100 tokens of  made up  that this PV in COCA’s spoken and academic registers. A 2 by 5 Chi-Square test  of th thee me mean anin ing g di dist stri ribu buti tion onss (r (rep epor orte ted d in Ta Tabl blee 4) yi yiel elde ded d a ve very  ry  2 significant result:   x (df    54 )   5  104.52,   p   ,  0.0001, with a Cramer’s V of  0.7229 0.7 229,, ind indica icatin ting g a cle clear, ar, sig signif nifica icant nt dif differ ferenc encee bet betwee ween n the se semant mantic ic distrib dis tributio utions ns of mak akee up in th thee tw two o re regi gist steers rs.. Al Alth thou ough gh th thee to toke kens ns in sp spok oken en Englis Eng lish h show a fairly fairly eve even n divis division ion amon among g the the four mea meanin nings, gs, the tok tokens ens in academic writing mean mostly  compose   (79%). This finding suggests clearly    compose  (79%). that the cross-register distribution of the different meanings of a PV is also important impor tant information. information. Unfortunately, Unfortunately, because of lack of space, this study  is unable to offer a close examination of this important issue. Furt Fu rthe herm rmor ore, e, as le lemm mmat atiz ized ed le lexi xica call it item ems, s, th thee 15 150 0 PV PVss ar aree li list sted ed  without information regarding their uses in different tenses, for example,   versus  made up  versus   versus  making up . This latter information is very  make up  versus important for language learners or teachers when deciding which form to 7

 All the examples here and in the following are from COCA. 676

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TABLE 4 Distribution of the Major Meanings of the First 100 Tokens of  Make Up  in   in the Spoken and  Academic Registers

Spoken  Academic

Compensate

Compose

Decide

Fabricate  

26 18

12 79

27 1

25 2

 

Other 10 0

focus on, because the dominant tenses in which specific PVs are used sometimes differ substantially. substantially. For instance, in COCA, although turn out  is  is used roughly 50% of the time in the past tense,  go ahead  appears   appears 93% of  thee ti th tim me in th thee pr preese sen nt te tens nsee. Th Thus us PV PVss li like ke turn around ma mayy be goo good d ite items ms for in inst stru ruct ctio ion n in te teac achi hing ng th thee pa past st te tens nse, e, wh wher erea eass PV PVss li like ke go ahead may be best used for teaching the present tense. Again, for lack of space, this study is unable to offer a detailed treatment of the tense distribution of  the PVs. Clearly, although the lemmatized list of the 150 PVs is a useful source for learning the most common PVs in general, English learners may still need to seek further semantic or usage information of the PVs  when learning these PVs. There are some useful sources they can turn to for help in this regard, including PV dictionaries and online sources like the Wordnet Search (Miller, 2008). Concer Con cerni ning ng the dis distri tribut butio ion n pattern patternss of the 85 si sign gnifi ifican cantly tly une uneven venly ly or  very unevenly distributed PVs, almost all of them appear primaril primarilyy in fiction and spoken English, the two registers that record the highest  overall use of PVs. Sixty of the 85 (70.59%) occur mostly in fiction and 22 (25.88%) in the spoken registers. Only three (3.53%) appear significantly  more frequently in the other three registers (two in academic writing and one in newspapers). Because of their rarity, the latter three deserve our atte at tent ntio ion n fi firs rst. t. Th Thee tw two o PV PVss th that at oc occcur ma maiinl nlyy in ac acad adeemi micc wr writ itin ing g ar aree bring  about   and   carry out .   Bring about   is used so predominantly in academic  writing that its frequency in academic academic writing (27.44 PMWs) PMWs) is many times (v (va ary ryiregisters. ing fr fro om 3Also to 10 time ti messmentioning ) more th than anhere its fr freeisqu quen enci cie es inout  ea,ch of th theeevenly  othe ot herr four worth that  a fairly  point  point distributed PV, registers its highest frequency in academic writing as well. It is important to note that  carry  and point out  are  are also found to be used  carry out  and most frequently in academic writing in the study by Biber et al. (1999). The reason that  bring  bring about  is  is not on their list seems to be that it does not  have at least 40 tokens PMWs in any of the registers, the criterion of  inclusion in their study. Biber et al.’s (1999) analysis also shows that  take    take    are more common in academic writing than in on, take up,   and  set up  are conver con versat sation ion.. How Howeve ever, r, in thi thiss st study udy,, onl only  y   take shows thi thiss pat patte tern rn take up   shows together with  set out,  likely because of the data in the spoken register of  COCA are mostly from TV or radio programs, not from conversations, as  was the case for Biber et al.’s spoken corpus data. Obviously, all these PVs PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

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deserve attention in academic writing teaching materials. In addition, fairly evenly distributed PVs that have a substantial frequency in academic  writing (e.g., break down, carry on, follow up, make up, rule out , and  sum up ) should also be considered. Thee on Th only ly si sign gnif ific ican antl tlyy un unev even enly ly di dist stri ribu bute ted d PV th that at ap appe pears ars mo most  st  frequently in newspapers is   pay off ,   but there are several in the fairly  even ev enly ly di dist stri ribu bute ted d gr grou oup p th that at cl clai aim m th thei eirr hi high ghes estt fre frequ quen ency cy in th thee newspaper register:  grow up, take over, shut down, wind up, turn down, fill  out,   and   come off .   Most of these PVs are expressions used to describe business busine ss dealings. dealings. As such, it is under understanda standable ble that they often find their  way into news. This finding helps illustrate the ‘‘field’ ‘field’’-specific ’-specific nature of  the use of some PVs, an issue Celce-Murcia and Larsen-Freeman (1999, p. 434) have addressed in some detail. Magazine is the only register in  which none of the significantly unevenly distributed PVs is used most  frequently. Yet, quite a few PVs (7) in the fairly evenly distributed group each record their highest frequency in this register, including break down, break up, build up, check out, set up, sum up, stand out,  and  take on . The fact  that these PVs all come from the fairly evenly distributed group can perhaps be explained by the mixed nature of this register. Magazine articles cover a variety of topics, and different magazines have different  target audiences, making their contents quite diverse. The 60 significantly unevenly distributed PVs that occur most often in fict fi ctio ion n ar aree a ve very ry la larg rgee gr grou oup, p, bu butt a ma majo jori rity ty of th them em (o (ove verr 40 40)) ar aree movement or action expressions, for example,   look a/round, look up, sit  describing human actions consti constitutes tutes down, stand up, and walk out . Because describing a ve very ry la larg rgee par artt of fi fict ctiion on,, it is tr tru uly be befi fitt ttiing of fi fict ctio ion n to ma make ke an ex exte ten nsi sive ve usee of th us thes esee act ctio ion n PVs Vs.. It is ag agaain impo port rtan antt to no note te th thaat so som me of th theese PVs are polysemous (e.g.,   look up ), ), but they are used mostly as movement  descriptions in fiction. Of the first 100 tokens of   look up   in the fiction register, regis ter, 98 are about upward vision or head movement, movement, as in the exam example ple ‘‘W ‘Whe hen n Bi Billly op open ened ed hi hiss ey eyees an and d lo look oked ed up , al alll he cou ould ld se seee ou outt th thee win indo dows ws  were stars.’ stars.’’’ Of course, not all action PVs appear appear most frequently frequently in fiction. fiction. Some are more common in spoken English. For example,  come down, go in , among others, are used most frequently in spoken English. In fact, the majority of the most frequent PVs in fiction also show a high degree of  frequency in spoken English and vice versa, largely due to the fact that a  subs su bsta tant ntia iall po port rtio ion n of fi fict ctio ion n is ma made de up of di dial alog ogue ues. s. St Stil ill, l, so som me ac acti tion on PV PVss occur almost exclusively in fiction, with just a few tokens (all in the single digits) in the other registers, including  call out, hang up,   and  sit back,  and they are largely mono-meaning, used for depicting actions. In contrast, polysemous PVs that can be used to either describe actions or express other meanings are common in both spoken English and fiction.  Another  Anot her PV that bear bearss some discussio discussion n here is  come on . In Biber et al.’s (1999) study, it is the most frequent PV in spoken English, but in COCA  678

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and the BNC,  go on  is   is the most frequent spoken PV. What is particularly  striking about  come  is that its frequency in spoken English in the LSWE   come on  is corp co rpu us (the cor orp pus Bibe berr et al al.. used) is ov oveer 30 300 0 PMWs; 26 266 6.9 .97 7 PMWs in the spoken part of BNC; but only 83.67 PMWs in the spoken register of COCA. The most likely explanation for its extremely high frequency in the LSWE and the BNC is that, as pointed out earlier, the data in the spoken register in the two corpora are primarily taken from face-to-face conversations,  whe  whereas reas the data in spoken spok registe ister r asti ofting COCA consist mostly mos tly of public pub spee sp eech ch me medi dium ums s li like kethe radi ra dio o oren TVreg broa br oadc dcas ng, , a mu much ch mor ore e fo form rmal al ty type pelic of  spoken language. The corpus examples of  come  come on  provided   provided by Biber et al. (1999), such as ‘‘Come on , let Andy do it’’ and ‘‘Come on , let’s go’’ help demonstrate the conversational nature of their spoken corpus data.

CONCLUSION: IMPLICATIONS AND LIMITATIONS This study has offered a comparati c omparative ve examination of the usage patterns of the most frequently used PVs in American and British English and across registers. Besides validating many of the results of Biber et al.’s (1999) and Gardner and Davies’ (2007) studies, it has provided some new  information about the use of PVs and a comprehensive list of the most  common PVs in American and British English, one that complements those offered by Biber et al. and Gardner and Davies with more items and more usage information. In addition, it also presents a cross-register list of  the most frequent PVs, showing in which register(s) each of the PVs is used primarily. English English learners or teachers can elect to use the lists of the 150 most common PVs in ways that best meet their learning purposes. For example, for a language curriculum or program with a general learning purpose, either the American or the British overall frequency list may be used as a reference guide, depending on whether American or British Engli Eng lish sh is ch chos osen en as th thee ta targ rget et En Engl glis ish h va vari riet ety. y. Fo Forr ES ESP P pr prog ogra rams ms,, however, one of the register-specific frequency lists (e.g., newspapers or academic texts) can be used as the guide. Currently, the frequency order is based entirely on the PVs’ overall frequency in COCA. To derive the correct frequency order of a register-s register-specific pecific list, one can copy the desired reg re giste terr li lisst to toge geth ther er with th thee PV it iteems ms,, pl plac acee th them em in an Ex Exce cell spre sp read adsh shee eet, t, an and d ha have ve th thee ra rank nk or orde derr ad adju just sted ed ac acco cord rdin ing g th thee PV PVs’ s’ frequencies in the register using the sorting function. The following are some additional pedagogical implications. 1.

Although Althou gh there there are are some PV usag usagee and freque frequency ncy diffe differen rences ces betwe between en  American and British English, the most common PVs are generally  rather similar between the two English varieties. Thus, except for those aforementioned usage differences, learners or teachers of English need not worry about the problem of learning PVs that are useful only in  American or British English.

PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

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2.

3.

4.

5.

6.

Althou Alth ough gh PV PVss th that at sh show ow a wi wide derr an and d mo more re ev even en di dist stri ribu buti tion on ac acro ross ss registers usually should receive more attention and perhaps be learned first, unevenly distributed PVs may actually deserve special attention for ESP learners, learners, beca because use of their high frequency frequency in the regis register(s ter(s)) that are the ESP learners’ focus. Learners Lear ners of English English shoul should d be made aware that the use of of PVs is registe registerr and an d fi fiel eld d se sens nsit itiv ivee so th they ey ca can n ap appr proa oach ch PV PVss mo more re ef effe fect ctiv ivel elyy an and d appropriately. For students learning academic writing in English, it is important to know that, although PVs are generally not common in formal writing, there are a few PVs (e.g.,  carry out  and  and  point out ) that are actually very useful in academic writing, and it will be to the students’ advantage to gain command of them. Writing teachers may want to purposely include these PVs in their teaching. Learne Lea rners rs should should focu focuss mostly mostly on polys polysemo emous us or idioma idiomatic tic PVs, PVs, becaus becausee mono-meaning and literal meaning PVs are not only easy to understand but are limited in context and function, as shown in the usage patterns of action PVs uncovered in this study. Lea earn rneers sh shou ould ld al also so un unde ders rsta tand nd th that at th thee va vari riou ouss mea eani ning ngss an and d functions of polysemous PVs are also often register specific, as in the case of   make up  discussed   discussed earlier. Learners or teachers should consult   various sources such as PV dictionaries and online sources like Wordnet  3.0 to become familiar with the different meanings, especially the key  meanings of the PVs they are learning. Learne Lea rners rs can can also take take adva advanta ntage ge of free free onlin onlinee corpor corporaa such such as COCA  COCA  and the BNC as useful sources for learning and practicing PVs, especially  their the ir dif differ ferent ent mea meanin nings. gs. For exa exampl mple, e, stu studen dents ts ca can n enh enhanc ancee the their ir ability in distinguishing the different meanings of a PV by going through concordance lines of a PV query to determine the meaning of each specific token. Such exposure to PVs can also help learners become more mo re fa fami mili liar ar wi with th PV PVss an and d th then en mo more re co comf mfor orta tabl blee in us usin ing g th them em,, hence helping overcome their inclination to avoid PVs. Furthermore, some useful strategies for learning PVs have been suggested, such as studying the cognitive motivation of the use of the AVPs in PVs to help bet better ter gra grasp sp meanin mea nings gscollocates of PVs (Ko  vecses & Szabo ´ , 1996) and and examining thethe typical noun of´´PVs to better understand retain idiomatic PVs.

Limitations of the Study and Implications for Future Research First, limited by space and research design, this study provides only  thee lemmatized most com th omm mon PVs, and it does not pro rovvide an examination of the use of the various meanings of those polysemous PVs across various registers. A tense-specific list and an analysis of the  various meanings of o f the PVs can help better understand how ho w the various tenses and meanings of a PV are used, including information such as in 680

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 which register or registers each of its tense forms and meanings is most  frequently used. Second, as is the case in Biber et al. (1999), the crossregi re gist ster er co comp mpar arat ativ ivee st stud udyy of PV PVss in th this is st stud udyy co cove vers rs on only ly br broa oad d categ cat egori ories es,, of offe feri ring ng li litt ttle le in infor forma mati tion on on th thee PV PVss us usag agee pa patt tter erns ns in spec sp ecif ific ic fi fiel elds ds,, su such ch as ai air-t r-tra raffi fficc co cont ntrol rol.. In th thee fu futu ture re,, mo more re fi fiel elddspecific comparative studies are needed.  ACKNOWLEDGMENTS I thank the three anon anonymous ymous reviewers reviewers and  TESOL Quarterly  Editor   Editor Alan Hirvela for thei th eirr ex extr trem emel elyy va valu luab able le co comm mmen ents ts an and d su sugg gges esti tion ons. s. Th They ey ha have ve he help lped ed me significantly enhance the quality of this article. I also thank one of the reviewers for suggesting the dispersion/adjusted frequency test and Professor Stefan T. Gries for allowing me to use his Dispersions2 program.

THE AUTHOR  Dilin Liu is Professor and Director of the Applied Linguistics/TESOL program at  the University of Alabama. His main research interests include the learning and teaching of lexis and grammar, especially corpus-based description and learning of  lexicogrammar.

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Gries, S. T. (2008a). Dispersions and adjusted frequencies in corpora.  International   Journal of Corpus Linguistics, 13 , 403–437. doi: 10.1075/ijcl.13.4.02gri. doi:  10.1075/ijcl.13.4.02gri. Gries, S. T. (2008b). Dispersions2 (statistical program) Retrieved from http://www. linguistics.ucsb.edu/faculty/stgries. Gries, S. T. (2010). Useful statistics for corpus linguistics. In A. Sa´nchez & M. Almela  (Eds.), A mosaic of corpus linguistics: Selected approaches  (pp.  (pp. 269–291). Frankfurt am Main, Germany: Peter Lang. Hulstijn, J. H., & Marchena, E. (1989). Avoidance: Grammatical or semantic causes? Stud St udies ies in Se Seco cond nd La Lang ngua uage ge Ac Acqu quis isiti ition on,, 11, 24 241– 1–25 255. 5. do doi: i:   10.1017/S0272263 100008123. Ko´´ vecses, Z., & Szabo´ , P. (1996). Idioms: A view from cognitive linguistics.  Applied  Linguistics, Lingu istics, 17 , 326–355. doi:  doi:   10.1093/applin/1 10.1093/applin/17.3.326. 7.3.326. Laufer, B., & Eliasson, S. (1993). What causes avoidance in L2 learning: L1–L2 Studies es in Se Seco cond nd La Lang ngua uage  ge  differ dif ferenc ence, e, L1– L1–L2 L2 sim simil ilari arity, ty, or L2 com comple plexit xity? y?   Studi Acquisition, 15 , 35–48. doi:  doi:   10.1017/S027226310001 10.1017/S0272263100011657. 1657. Liao, Y., & Fukuya, Y. J. (2004). Avoidance of phrasal verbs: The case of Chinese Language ge Lea Learni rning ng,, 54 , 19 learn le arners ers of Eng Englis lish. h.   Langua 193– 3–22 226. 6. do doi: i:   10.1111/j.14679922.2004.00254.x. Liu, D. (2003). The most frequently used spoken American English idioms: A corpus TESOL Qua Quarter rterly, ly, 37 , 671 study anal analysis ysis and impl implicati ications. ons.   TESOL 671–70 –700. 0. doi doi::   10.2307/ 3588217. Liu,, D. (20 Liu (2008) 08).. Lin Linkin king g adv adverb erbial ials: s: An acr across oss-re -regis gister ter stu study dy and its im impli plicat cation ions. s. International Journal of Corpus Linguistics, 13 , 491–518. doi:  doi:  10.1075/ijcl.13.4.05liu 10.1075/ijcl.13.4.05liu.. Longm Longman an phras phrasal verbs dicti dictionary onary..  English (2000). (2000 ).phrasal Harlow, Harl ow, England: Engl Pearson Pears on Educa Education tion verbs inand: use.  Cambridge, McCarthy, M., &alO’Dell, F. (2004). England: Cambridge University Press. Miller, G. (2008). Wordnet Online (Version 3.0, first launched in 2006) Retrieved from http://wordnetweb.prince http://wordnetweb.princeton.edu/perl/webwn. ton.edu/perl/webwn. Moon, R. (1998).  Fixed expressions and idioms in English . Oxford, England: Clarendon Press. Oxford Oxf ord phr phrasa asall ver verbs bs dic dictio tionar naryy for Eng Englis lish h Lea Learne rners. rs. (20 (2001) 01).. Ox Oxfor ford, d, Eng Englan land: d: Oxford University Press. Quirk, R., Greenbaum, S., Leech, G., & Svartvik, J. (1985). A comprehensive grammar of   the English language.  London, England: Longman. Side, R. (1990). Phrasal verbs: Sorting them out.   ELT Journal, 44 , 144–152. doi: 10.1093/elt/44.2.144. Spears, R. A. (1993). NTC’s dictionary of phrasal verbs and other idiomatic phrasal  verbs and phrases. Lincolnwood, IL: National Textbook.  Wyss, R. (2003). Putting phrasal verbs into perspective. TESOL Journal, 12 , 37–38.

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TESOL QUARTERLY  

 

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   r    e   e     d    r    c    o   n    e    r       6    e     k    f     3     7     4     2     2     1     2     8     f     0     1     2     5     1     2     5     2     2     4     9     9     1     0     7     7     7     1     0     6     1     2     2     7     1     1     9     1    n     i    a    d     R    r    e     d    r    o     7     9     3     2     1     0     4     2     3     0     2     9     6     4     0     5     1     3     5     k     1    6     4     C    n     1    3    5    9    4    8    1    6    7    5    2    2    3    1    2    2    1    3    1    2    1    2    2    3    1    1    1     N    a     B    n     R     I     3     2     l     3     5     1     7     7     8     9     2     1     4     1     8     1     9     6     3     2     1     3     7     8     0     7     9     2    a  .  .    4    t     8    9     9     2     8     9     5     5     4     6     2     9     7     6     4     6     3     5     3     5     1     1     0     5     4     5  .    9  .  .  .  .  .  .  .  .     3  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    o     4    9     7     6     9     4     0     5     9     6     9     8     0     2     5     7     1     1     4     3     5     8  .     4     9     4     8     7  .     T     1    8    7    5    8    6    4    7    6    1    1    4    3    4    4    4    5    3    4    3    2    4    3    3    4    4    7    1     2    r    e     d    r    o     A     k     1    2    3    4    5    6    7    8    9    0     1    1     1    2     1    3     1    4     1    5     1    6     1    7     1    8     1    9     1    0     2    1     2    2     2    3     2    4     2    5     2    6     2    7     2    8     2     C    n    a     O    R     C    n     I     8    0     4     8     l     4     4     4     3     1     7     1     6     1     8     3     6     7     1     0     0     6     4     7     3     1     2     2     2     7     8    a  .  .  .  .    1    t     4     7     3     4     5     7     5     1     5     4     3     1     1     8     8     5     2     1     4     4     3     2     6     5     5     5     9     1  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    o     3     1     0     0     7     0     2     0     9     9     5     4     4     3     2     6     5     4     3     0     8     7     7     4     3     9     9     6     T     5     1    1    1    1    9    8    7    7    6    6    6    6    6    6    6    5    5    5    5    5    4    4    4    4    4    3    3    3     i    c     2    7    8    9     1    1    6    1    8    m     0     2     1     4     5     8     0     7    e     5     5     7     9     9     7     3     4     2     5     3     3     2     1     5     7     9     3     3     1     4     9     8     6     3     0     1  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    4     6     9     3     3     3     0     2     8     4     9     7     d     2     3     1     8     9     2     1  .     0     2     3     3  .     0     8     3     6     0  .  .     2  .  .  .  .  .  .  .    a    c     5    2    1    1    1    2    1    9    9    2    4    3    6    1    4    2    4    2    5    4    1    6    5    8    1    6    8    5     A     A     C     O    r     5     C    e    p     8    8    5    9    2    2    0    5    3    2    4    2    3    6    3    0    1    2    4     5     9     3     4     2     2     5    a  .    9    n    p     6     3     2     1     2     0     8     3     0     0     i     1  .  .  .  .  .  .  .  .  .    4  .    6  .    2  .    5  .    5  .    9  .    8  .    2  .    5  .    8  .    9  .    2  .    5  .    8  .    6  .    4  .    2  .    5  .    s     0    0    9    8    5    0    0    6    2    7     4     8     4     6     0     7     6     8     4     2     1     3     4     3     0     7     7     7    s    w    r     1     9     6     6     6     5     5     5     6     9     7     5     4     4     7     6     5     5     4     1     4     2     2     3     1     2     4     2    e    e    t    s     N     i    g    e    r    e    n     1    3    5    2    5    8    5    7    0    5    0    9    4    0    3    8    0    7    7    6    9    5    2    5    4    7    0    8    e     i    z     7     h  .    1  .    1  .    4  .    1  .    8  .    3  .    4  .    7  .    9  .     5  .    8  .    8  .    3  .     9  .    3  .    2  .    9  .    3  .    9  .    3  .    6  .    2  .    1  .    8  .    4  .    3  .    2  .    t    a    g     6     6     0     4     6     5     5     8     6     5     3     1     1     9     1     1     9     3     5     9     2     7     1     5     2     6     3     2    s    a     9     9     5     6     5     6     4     4     7     7     8     8     4     3     7     5     5     6     3     1     5     2     3     3     1     2     4     3    s     M    o    r    c    a    n    n     7     7     9    0     2     1  .     0     5     5  .    9  .     6  .     7     4  .    3     i    t    o     i    o     0     7  .     4     8  .     8     3  .     2     9  .     1     9     7  .     2     9     1     0     7     1  .     1     4     5     4     9    t     4     6     4     4     9     7     3     2     5     7     9     3    u    c     8    2    3    2    4  .  .    9  .  .  .  .    7  .  .  .  .  .  .     8     2  .     4     6     6  .     7     8  .    7  .     6  .     i     b     9     6     6     0     5     9     0     0     8     1     5     4     0     9     6     2     1     7     2     0     2     2     2     7     0     4     5     1     i     F     1    2    1    1    1    9    9    1    3    6    5    6    1    9    4    7    6    4    9    2    6    1    1    8    1    6    4    8    r    t    s     i     D    n     1    8    8    2    1     5    3     7    5    e     7     3     7     7    0     4    1    5    6    5    9    5    7    1    1    3     5     2     5     k  .    6  .  .     5  .     9  .     0  .     2  .     9  .    3  .     2  .    7    1     8     4     2     6     3     3    o     6     8     1    0    0    2    3    9    1  .  .  .  .     2    1    6  .  .  .  .    6  .    8  .     4  .    7  .    7  .    4  .    6  .     5  .     1  .    7  .     1    0    5    5    9    6    6    2    9    9    7    2    2    2    1    5    4    2    0    6    0    5    1    7    3    3    2    6    p     S     3    1    2    2    1    1    1    1    7    8    6    8    1    1    7    6    5    8    9    1    7    5    5    5    8    7    5    3

    *     *     *     *      1     *     *      1     *      1    t     k      2      1      2    t      2      1      2      2      1     *     2      3    u   n      3      3    t      2      1      1     p      2    c    p      2    t    t    t     p     2      1    u     p    n     k      2    a    u    k    u   u     p    b    n   n    p    u    p    c    p    o    w      2    u   n    t     i      1    u     f    u    f      2    t    o    u    o    c    o    o     2    w    o     p    e    u    o    o    u     p    u    u    a    t    u    o    e    u    b    r    o    u    e    o    w   o    e    n    a    u    e    e    e     d    s    o    k    e     k    e    e    e     b    o    w    n     d    n     k    u    c    m   m     d     V     i    n   m     i    o    o    t    r    t    m    k    v    a    d    t    o    g    t    t     k    m    o    k    o    p    c    o    c    o    g    o    f     i    c    o    g    o    p    g    r    s    e    t    u   g    e    c    o    t    a    i    g    m   e    n   g    e    l    o    f     i     i    s    g    e    t    a    c    o    g    o    h    s    t    a     P    g      2

PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

683

 

   r    e   e     d    r    c    o   n    e    r      e     3     k    f     4     9    2     6    0     0    4    2     7    2    6    3    4    4    3     3     f    n     i    a    d     1    4    2    2    4    8    2    1    2    2    3    2    2    1    7    0    5    1    6    1    7    2    7    1    1    3    1    3    2     R    r    e     d    r    o     8    2    8    8    3    1    5    0    8     k     1    4     3    3     3    6     5    9     2    2     4    2     6    4     2    1     1    1     6    3     7    4     6    8     1    3     4    6     3    4     4    0     4    9     5    1     4    9     4    2     1    7    5    6    6    5    4    9    7     C    n     6    a     N     R     B     I    n     l     3     0     7     1     0     7     0     5     6     9     2     6     1     2     0     5     3     3     3     2     2     7     5     5     9     1     8    a    t     1     4     9     4     6     4     0     6     9     1     9     6     8     1     2     9     9     3     9     4     3     1     5     6     0     6     8     8     3  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    o     8     7     0     2     7     6     2     6     1     3     6     3     5     5     2     8     1     4     6     4     0  .     4     6     4     6     9     1     0     3     T     6     4    3    3    1    3    2    1    4    5    1    1    1    4    2    2    2    2    1    2    2    5    1    1    1    1    1    2    1    1    r    e     d    r    o     A     k     9     2    0     3    1     3    2     3    3     3    4     3    5     3    6     3    7     3    8     3    9     3    0     4    1     4    2     4    3     4    4     4    5     4    6     4    7     4    8     4    9     4    0     5    1     5    2     5    3     5    4     5    5     5    6     5    6     5     C    n    a     O    R     C    n     I     l     0     6     4     5     5     5     6     8     3     7     4     6     2     7     9     6     9     4     1     7     3     4     8     7     9     7     5     5    a    t     7     4     5     8     2     9     5     8     2     1     4     3     1     9     4     3     3     9     2     7     3     1     0     7     3     3     1     8     8  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    o     4     6     6     4     3     3     9     9     8     8     8     7     7     7     4     4     4     4     3     3     2     2     1     1     0     0     0     9     8     T     3    3    3    3    3    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    1    1    8     1     i    c     5    1    m     6     1     3     5    e     2     2     6     0     2     6     5     9     6     2     6     7     3     5     0     9     9     4     7     7     3     6     9     0     7     5     4     5     3  .  .  .  .  .  .  .    7     7     8     6     5     9     6     3     3     2     9     5     1     5     3     8     2     1     4     3     4     6     d     4  .  .  .  .  .  .     2    6    2  .  .  .     2  .  .  .  .  .     1     7  .  .  .  .  .  .     7  .  .    a    c     1    5    6    2    5    7    3    6    1    7    3    4    2    3    4    9    8    3    1    1    3    6    4    2    8    2    1    2    7     A     A     C     O    r     C    e    p     7    3    7    9    9     6    1    3    6    6    1    4    2    1     5    2    7    8    2     0     0     1     9     2     3    a     7    n     5     5     2     2     0     9     9     4     8     2     4     i    p  .  .  .  .  .  .  .  .    3  .    5  .    3  .    4  .    9  .    2  .    7  .    3  .    7  .    1  .    7  .    9  .    9  .    7  .    4  .    2  .    5  .    7  .    s     2     0     5     9     8     7  .     2     7     0     3     5     7     6     0     2  .     8     1     5     2     5     9     9     8     7  .     0     2     7     7     1    s    w    r    e     3    1    2    9    3    1    2    2    4    1    1    2    2    6    2    2    1    1    2    1    1    1    1    7    2    1    1    1    2    e    t    s     N     i    g    e    r    e    n     0    4    5     0    1    2    2    8    8    5    8    2    8    3    3    3    1    4    2    2    8    2     2     9    6    9    e     i    z     5     4     8     2     h  .    9  .    2  .    9  .    9  .    4  .    9  .    0  .    0  .    4  .    0  .    4  .    9  .    5  .    6  .    8  .    0  .    2  .    1  .     6  .    7  .    2  .    1  .    6  .    4  .    9  .    t    a     6     3     8    g     3     0     4  .     6     9     6     9     6     0     8     6     9     1     0     0     7     5     8     3     3     8     6  .     0  .     6     5     1    s    a    s     M    4    2    2    9    2    1    2    1    2    2    1    1    2    1    2    2    1    1    1    2    3    1    1    9    2    7    2    1    2    o    r    c    a    n    n    o    o     9    2    7    4    9    0    2    3    8    1    7    0    6    9    6    6    9    2    8    3    2    5    7    9    0    2    8    9    5     i    t  .    3  .     8  .    1  .     4  .     1  .     3  .     0  .    1  .    9  .    1  .     0  .    1  .     4  .    u     i    t     0    c  .     3  .    3  .    3  .     2  .    2  .     1  .     5  .     7  .    5  .    6  .    0  .    8  .     7  .    9  .     2     b     4     1     4     6     5     1     3     2     3     5     7     4     0     7     3     4     5     1     8     5     7     3     1     3     3     5     i     F     3     9     5     2     3     7     6     1     2     7     7     6     4     9     3     3     3     7     2     2     2     2     2     7     3     3     1     3     2    r    t    s     i     D    n    e     3    6    2    6     4     9    8    2    1    7    5    3    7    6    8    6    0    1    7    3     3     8     1     2     2     8     2     k  .     4    6     3     4     0     4     0     5     8     0     4     1    o     6  .    7  .  .     9    8  .    9  .  .  .  .  .  .  .  .  .    7  .     7  .    9  .     9  .     2  .    7  .     9  .     9  .    7  .    9  .     1  .    9  .    6  .    4  .     0     9    2    0    1    4    3    4    6    0    2    2    3    1  .     5    4    4    4    8    2    5    5    8    2    1    8    9    5    7    p     S     4    4    6    1    6    3    3    2    3    2    2    3    2    9    3    3    4    1    3    3    2    3    3    1    2    4    1    2    1

     2

    d    e    u     E    i     L   n    t     B   n     A   o     T    C

684

     3     d     d    *      1     *    n     *    n    *    n     *      1     *     1     *     *      3     *     *    u    n     *     k      2     *    t     1    u     *    o    n    c     2      3      1      1      3      2      1      1      2      1     *      2    w    t      2    t     p    w     k    o    r      2    t    r     1    w      1     p    t    n    p    u   n     2     p    o    r    c    p    u   e    a      1    o    d    t     /    u     p    a    u     p    o    p    b    u   u    i    u   o    o    u    /    u     2     d     2    u    a    u   o    v    e     2    o    d    d    h     f    a    p    u    o    o    u     b     d    o    a     f     p    u    o    e    g    g    g    n    k     k    o    u    e    y     h    e    a    u    k    k    n     l    s     k    e    e    r    c     i     d    n    c    v    n     p     k     k     k    r     l     k    o    t     i    n   n   o    i    n   e    e    o    t    o    t    a    m     k    l    t    e    o    a    r     V    o    a    o    u   r    u   r    i    u   o    a    o    e    o    o    e    e    r    r     p    r    o    a     h    t    a    u    a    o    o    o     P    w   s    c    g    g    l    w   c    t     h    p    t    t     l     p    b    b    l     b    o    c    m    p    l    c    g    b    g    k      2

TESOL QUARTERLY  

 

   r    e   e     d    r    c    o   n    e    r      e     3    5    3    3    3    9    7    0    7    5    7    2    2    7    2    5    7    8    7    0    7    2     0    8    3    6    5    0     k    f     f    n     i    a    d     2    4    1    1    1    2    4    4    7    3    1    4    3    2    1    7    2    5    2    3    4    1    1    1    3    3    5    5    6     R    r    e     d    r    o     4     5     3     2     1     8     1     3     3     7     5     0     0     6     0     4     5     2     7     4     1     0     3     2     7     1     2     0     8     4     5     7     0     0     5     8     8     0     4     0     0     1     4     k     C    n     3    1    4    7    7    9    1    1    1    1    8    1    3    9    6    1    1    1    1    1    1    6    8    7    1    5    2    1    1    a     N     R     B     I    n

    l     4     4     7     3     1     1     7     0     0     0     8     1     4    a    t     0     5     9     7     6     5     1     6     9     0     9     6     9     6     2     4     6     0     6     4     8     0     8     1     0     1     3     4  .  .  .  .  .  .  .  .  .  .  .  .  .  .    1    o     6     4     2     6     7     2     9     6     8     4     6     0     1     5     9     7  .     0     3     3     0     6  .  .  .     1  .     6  .     6  .  .  .  .  .  .     5     2     4  .     0     7  .  .     T     8     2    9    2    1    1    1    4    9    4    9    1    8    2    9    1    2    9    5    9    9    6    1    1    1    7    2    3    4    2

   r    e     d    r    o     A     k     8     5    9     5    0     6    1     6    2     6    3     6    4     6    5     6    6     6    7     6    8     6    9     6    0     7    1     7    2     7    3     7    4     7    5     7    6     7    7     7    8     7    9     7    0     8    1     8    2     8    3     8    4     8    5     8    6     8     C    n    a     O     R     C    n     I     l     4     2     1     1     1     7     8     2     0     9     2     2     1     7     7     5     3     2     9     9     6     1     3     7     4     1     3     0    a    t     5     0     9     6     9     0     2     2     0     0     9     4     2     7     7     6     8     5     5     2     6     5     4     2     2     1     5     3     1  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    o     8    8    8    8    7    7    7    7    6    6    6    6    5    5    5    5    5    5    5    5    5    5    4    4    4    4    4    4    1     T     1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    4     1    c     i    m     4     9    e     5     2     9     5     4     3     5     9     1     3     1     0     2     7     4     4     2     2     1     2     6     4     1     7     6     0     9     0  .  .    6     3     7     1     6     4     9     1     9     4     7     1     9     1     2     4     2     9     5     1     4     3     3     0     7     8     3     6     d  .  .  .  .  .  .     7  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .     0  .  .    a    c     3    7    3    7    3    1    1    7    5    5    6    3    4    4    4    8    2    0    2    3    4    4    6    7    1    2    1    3    2     A     A     C     O    r     C    e    p     5    3    2    0     6    0    8    3    6    0    4    9     0     6    6     0    6    0    7     3     1     3    n    a     4     1     6     5     7     1     5     0     5     8     8     9     4     4     4     9     5     4     6     7     5    p     i  .  .  .  .  .  .  .    2  .  .  .    6  .  .  .  .  .  .    0  .    6  .    8  .    1  .    2  .    6  .    s     5     2     4     4     4     6     8     0     3     2     4  .  .     4     9     4     8     3     1     4     0     0     1  .  .     3  .     4  .     4     0  .     1     2     5     5    s    w    r     1     1     1     1     6     8     1     1     2     1     1     2     1     2     1     1     7     6     1     8     2     7     1     1     6     1     1     1     1    e    e    t     N    s     i    g    e    r    e    n     5    4    6    9     4    8    8    8    3    5    0    3    2    9    5     7    0     0    5    3    7     9     0    e     i    z     5     6     4     2     4     8     0     1     3     5     0     8     1     8     5     9     7     7     0     7     5     4     5     h  .  .  .  .  .  .    8  .  .  .  .  .  .  .  .  .  .  .  .    3  .    4  .     5  .    3  .    0  .    t    a     6     8     2     2     2     1    g     3     4     2     6  .     3     3     7     7     3     1     9     9     7     0     6  .  .  .     1     2  .     8     3  .     3     1     0     7    s    a     1    1    1    1    8    1    2    1    1    1    2    1    1    1    1    1    7    9    9    1    2    8    1    1    9    1    2    1    1    s     M    o    r    c    a    n    n    o    o     1    7    3    4    3    3    0    9    1    7    4    2    8    4    7    9    6    0    5    8     7    1    6    4    0     5    7     i    t     i     0  .     1     5  .     6     7  .     7     0  .     5     4  .     8     7  .    4     2  .    9     5  .     1     2  .    7     6  .     9     0  .     4     8  .    5     2  .     4     2  .    3     8  .    6     9  .    9     5  .     9     0  .    7     9  .     b    u     i    t     1    c     9  .     0     2  .     2     5  .     2     8  .     9     2  .     9     0  .     5     7  .    9     8  .     9     6  .    5     8  .     4     i     F     5     4     3     3     6     5     1     2     1     2     2     1     2     2     2     1     3     5     1     2     7     4     1     1     4     3     8     1     1    r    t    s     i     D    n    e     4    7    5    9     5    1     6    6    7     7    3     1    1    9    5     4     7     0     5     4     7     9     2     3     k     6     1     7     0     4     7     0     7     8     6     2     3     9     1     2     8     8  .  .  .  .  .  .  .  .  .  .  .  .  .  .    1  .    6  .    7  .    6  .    4  .     5  .    7    4  .    0  .     1  .    5  .    o     1     1     2     7     3     8     0  .  .     2     0     3     0     7     9     3     4     3     0    6    3  .     8    3    5    8  .     6     2    6  .     3     7     3    5    p     1     1     2     2     8     9     1     2     2     2     1     1     1     1     2     2     2     8     3     2     1     6     1     2     9     1     1     2     1     S

    d    e    u     E    i     L   n    t     B   n     A   o     T    C

    *     *     *    *     *     *      1     *    *    *     *    *     *     *     2     *    *      1     *     *     *      3     *     *      2      2     *      2     *     h     *     *      1    n      2     *      3      3     *     *    k      1      1      1    n      1    t     1      1      1     *    t     *    r      1    g      3    n   t     k     k     *     2     p    u     2      2     *    t     p    u   c    n    p    u   t    e    w    p    p     1    c     1     *    c     3      1     p    w    u    w      1    a    p    o    v    o    t    r    n    u    o     f    u    u   o    a    o    u   o    u     p    u    u    a    o    t     f    o      2     f     b    o    u    e     d    o    o     d    r     f     f    u     b    u     b    g    d    w   o    u    u    o     d     f    v     k    g    g    g     h    o     d    n    o     h    t     l    s    e    y    o    n   t     d    c    o    t    n    n    n     d    o    e    k    a     l    o    n    l     l    n    l    w    n    y     i    a    n     l     l    r    u    r    r    r    e     i    a    t     i    e    o     V    t    n    u   e    o    u   u   u   e    l     h    u    l    u    i    a    a    o    a    o    o    a    o    r    r    u   a    u    h    a     P     p    r    g    c    t     p    s    c    s    t    s    w   t     l    t     l    g    h    g    h    p    h    b    b    p    h    b    t     h

PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

685

 

   r    e   e     d    r    c    o   n    e    r      e     9    3    0    7    0    2     k    f     1    9    1    0    8    3    9     7    5    5    0    9    7    2    5     0    6    4    0     f    n     i    a    d     1    2    1    2    5    5    6    0    1    1    2    1    1    7    4    8    1    1    3    2    5    3    1    1    2    3    3    1    6     R    r    e     d    r    o     8    5    9    6     1    1     4    4     4    7    4    6     0    7    6    8    7     1    7    2    0     1    6    9    0    6     2    8    5     4    7    5    3     1    2    9     4    0     0    5     k     C    n     6    6    9    1    1    1    8    9    1    7    7    8    1    2    5    1    8    8    7    1    4    1    9    9    1    8    1    1    5    a     N     R     B     I    n     l     1     3     6     3     7     2     1     0     9     0     9     3     8     7     4     6    a    t     1     3     9     6     8     9     5     4     8     6     6     2     9     5     6     8     8     0     1     1     7     1     0     1     4     6     2     1     9  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    o     2     9     8     8     7     0     7     2     9     3     1     6     9     4     5  .  .  .  .     1     0  .     3     3     1  .     8     8  .     1     1     4  .     0  .     0     0  .     2  .  .     T     1    1    9    7    4    3    1    1    9    1    1    1    7    3    1    8    1    1    1    5    2    3    1    1    8    1    2    9    7     1    r    e     d    r    o     0     1     2     3     4     5     6     7     8     9     0     1     2     3     4     5     8     9     9     1     2     3     4     5     6     7     8     9     0     0     0     0     0     0     0     0     0     0     1     1     1     1     1     A     k     7     8    8    8    8    9    9    9    9    9    9    9    9    9    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1     1     C    n    a     O     R     C    n     I     l     3     3     3     4     3     3     1     8     3     0     6     1     4     9     1     7     5     7     5     2     2     9     6     5     8     2     6     7    a    t     8     5     4     4     1     0     8     7     5     5     4     3     1     0     7     7     5     3     2     2     2     9     8     8     8     6     3     2     0  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    o     0     4     3     3     3     3     3     2     2     2     2     2     2     2     2     1     1     1     1     1     1     1     0     0     0     0     0     0     0     T     1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    0     1    c     i    m    e     2    6    5    8    3    3    1    3    9    0    9    5    5    6    3    1    7    4    6    0    4     4     9     2     1     7     6     3  .    4    2     1     9     3     9     0     5     9     6     2     8     6     6     5     8     3     3     1     1     1     2     1     1     3     8     2     7     9     d  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .     7    7  .  .  .  .  .  .  .  .    a     3     1     1     2     4     3     0     2     4     2     4     1     2     8     4     2     2     2     1     3     2     2     5     4     9     1     8     5     1    c     A     A     C     O    r     C    e    p     5    6     8     4    3    5     7     0     2     9     7    a     9    9    9    4    n     1     9     4     4     3     6     0     6     8     4     3     3     9     4     9     9     1     7     8     1     7     1     6     5     i    p  .    0  .  .  .  .  .  .  .  .  .  .  .    0    s     6     5     9     0     8     8     6     3     3     8     9     5     9     4     2     5     5  .  .  .     4     3  .  .     7     3  .     2  .     1     0     2  .     0  .  .  .  .  .  .  .     0  .     1     1  .    s    w    r     8     9     5     1     1     6     1     1     1     8     1     9     1     1     1     9     1     4     3     8     6     4     5     9     1     9     1     1     6    e    e    t    s     N     i    g    e    r    e    n     3    1     8    3     2     9     4     3     9     9     9     7     3     6     5    e     i    z     9     9     3     7     4     4     8     5     9     8     0     4     0     4     0     3     6     1     7     0     6     7     0     5     2     6     0     2     h    a  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    2    t    g     1    0    5     4     3     1     6     2     6     0     6     1     5     2     0     3  .     1     6  .  .     2     2  .     2  .     0     2     4     2  .  .  .     3  .  .     0  .     3  .  .     0  .    s    a     1     1     8     1     1     7     6     1     1     9     1     8     1     1     1     1     9     7     5     1     8     7     1     9     1     9     9     1     6    s     M    o    r    c    a    n    n    o    o     7    2    1    9    3    9    1    9    4    1    6    2    6    1    4    1    6    7    7    2     2    8    6    7    6     3    1     i    t     i  .     6  .     4  .     9  .    8  .     7  .    0  .     9  .    5  .    4  .     5  .     0  .     4  .     4  .     3  .     2  .    8  .     6  .    4  .    4  .    1     b    u     i    t     8    c  .     9  .     5  .     1  .     4     0  .    4  .     8     9  .     3     5     4     0     3     0     7     8     5     4     2     7     8     1     6     8     0  .     3     2     4     5     2     7  .     2     7     i     F     2     2     3     2     1     4     5     1     1     2     1     2     1     1     1     2     2     3     3     2     2     3     2     1     1     1     6     1     1    r    t    s     i     D    n    e     4    3    0    7    2     8    2    5    5     5    6     2     1    5    9    6     9     2     6     8     2     k     5     0     0     9     2     4     0     3     6     2     2     0     2     2     3     8     2     7     6     7  .  .  .  .  .    8  .  .  .  .    9  .  .    5  .  .  .  .  .  .    9    0  .     9  .    4  .     5  .    o     0     2     6     4     9     8     1     9     8     2     6     3     0  .  .    3    4    6    8    9    1    1  .    4    4  .  .    0    0  .  .     5    7  .     5     5    1    8    p     S     1    2    1    1    2    5    5    1    1    1    1    1    2    1    9    1    1    6    8    1    1    8    8    1    7    1    1    1    1

    d    e    u     E    i     L   n    t     B   n     A   o     T    C

686

    *     2      2     *    *    t     *    *     1     *    *      1     *     *     *     *    g    *      2     d    n     *      1     *     *    u      1    n    n      3     *     *     1      3      2    w   t    n    *     *     2      1     *    r    t      1     *     *    t    o      1      1      1      1    e    t    t      2      3      2     k    n   o    u    l    n   v    n   u     3    o    t    u   u      2    t    u    t     3    w      2    o    p    k     p    f     b    c    u     2     p    n    w    a    c     d    o    a    u    o    o    w      1    n   u   o    u    f    o    a    o    i    o    u    r     d    o    o    n    a    u    u     f    o    o    o    o     p    o     b    o     f     d    n    o     b    e    e    g    g     k    e    v    t     i    w   y    t    n   n    p    d    n   n   e    y    a    /    o     k    u     d    s    t     d     k    r    n    a    r     l     p    t    n   a    u   r    i    e    y    i    a    m    l     l    t    r    c    m    t    e    a    e    o    a    n    t    o    r     V    u   t    u    e    e    e    a     l    r    r    r    o    a    a    o    t     i    e    e    u     h    t    t    u    u    a    g     P     p    g    c    m   s    c    s    t     b    p    s    g    b    c    s    k    r    m   s    t     b    s    l     b    s    c    p    b    o      2      2

TESOL QUARTERLY  

 

   r    e   e     d    r    c    o   n    e    r      e     7     3    1    9    3    4    8    7     8    0    2    6     k    f     3     9     1     8     7     7     6     1     f     1     1     6     5     1     2     1     4     1     1     8     8     1     7     4     2     4     5     3     4     9     4     9     4     5     8     7     1     2    n     i    a    d     R    r    e     d    r    o     5    4    2     1    0    9     2    8    8     8     9     6     7     9     9     5     7     3     1     3     1     6     3     5     0     9     4     0     1     9     3     7     4     2     3     0     3     3     7     6     2     1     3     4     3     2     2     k     C    n     1    1    1    6    1    1    1    7    1    1    1    3    1    5    8    1    1    8    1    1    3    9    1    9    8    5    1    1    1    a     N     R     B    n     I     l     5     3     5     8     1     6     2     6     2     8    a    t     6    1    9    5     0     3     5     0     0     5     2     1     8     5     3     2     0     3     8     7     2     3     1     4     7     4     1     6     4  .  .  .  .  .  .  .  .  .  .  .    0    o     0     3     1     6     5     3     7     5     7     0     7     1     0     0     6     3     7     1  .  .  .     5  .  .  .     3  .  .  .     6  .     7     2  .  .     2  .  .     7     0  .     0     2     8  .  .  .     T     8    5    8    1    5    2    8    1    4    8    7    2    5    1    1    5    5    1    5    5    2    1    5    1    1    1    5    5    7    r    e     d    r    o     6    7    8    9    0    1    2    3    4    5    6    7    8    9    0    1    2    3    4    5    6    7    8    9    0    1    2    3    4     A     k     1     1    1     1    1     1    1     1    2     1    2     1    2     1    2     1    2     1    2     1    2     1    2     1    2     1    2     1    3     1    3     1    3     1    3     1    3     1    3     1    3     1    3     1    3     1    3     1    4     1    4     1    4     1    4     1    4     1     C    n    a     O    R     C    n     I     l    a    t     1     6     4     3     6     3     5     1     9     9     7     7     6     7     6     7     2     7     5     2     3     6     3     0     9     2     8     8  .    0    o     0     7     1     0     6     4     4     2     2     1     1     1     9     9     7     7     6     4     0     9     8     7     6     5     5     9     6     5     2  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .     T     0     1    9    9    9    8    8    8    8    8    8    8    8    7    7    7    7    7    7    7    6    6    6    6    6    6    5    5    5    5     i    c    m    e     5     2     8     6     4     1     5     2     6     4     2     8     3     1     2     2     7     8     4     9     3     3     4     5     7     4     7     4     6     3     9     5     4     1     5     7     6     2     4     8     5     9     0     6     2     6     8     7     5     9     0     5     7     9     7     7     2     5     d  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .    a    c     1    1    3    5    3    5    0    8    3    2    1    1    2    3    9    2    0    4    2    1    4    7    1    1    0    3    1    2    1     A     A     C     O    r     C    e    p     6     2    a     2    5    7    2    6    6    n     8     6     9     8     4     3     7     0     4     6     4     2     3     3     0     5     4     1     3     4     1     8     9     i    p  .  .  .    6     2    s     5     5     4     0     7     2     8     6     5     5     9     2     4     5     0     4     5     4     8     9     9     3     3     8     4  .  .  .  .  .     0  .  .     0  .  .  .  .  .  .  .     2  .  .  .  .  .  .  .  .  .  .  .  .    s    w    r     6     7     7     5     9     1     5     9     1     5     5     5     7     6     8     7     1     6     5     1     5     5     6     4     4     6     5     3     4    e    e    t    s     N     i    g    e    r    e    n    e     i    z     9    1    2    1    3    8    0    2    8    0    1    8    8    5    3     6     0     0     6     5     1     6     3     6     0     5     0     1     9     h  .    9    t    a     7     1     7     9     9     5     5     2     6     9     0     1     3     9     9     9     5     2     9     9     1     4     4     5     3     8     1     2    g  .  .  .  .  .  .  .  .  .  .  .  .  .  .     1  .  .  .  .  .  .  .  .  .  .  .  .  .  .    s    a     5    8    7    8    7    8    5    8    8    8    6    5    6    5    1    5    7    8    6    2    6    3    5    6    4    6    4    6    4    s     M    o    r    c    a    n    n    o     i     4  .    6  .     7  .     8     5  .    2     9  .    5  .     3     1  .     9     5  .     7     6  .    7     5  .    9  .    2     0     6  .     6     4  .    7     3  .     0     5  .     9     2     5  .    3    t     i    u    t     8    o  .     6     8     0     1  .     2     6     7     3     4     7    c     i     b     4     1     8     6     4  .     6     3  .     4     0     6     6     4     0     7  .     2     3  .     8  .     1     5     7  .    5  .    6  .     6     5     4  .    7  .     9     0  .     i     F     2     1     1     1     2     1     1     1     1     1     1     8     1     2     1     1     7     1     1     9     2     1     2     8     7     4     8     7     6    r    t    s     i     D    n    e     9    6     6     8     0    2     8     1     0     k     0     8     8     0    4     2    0    3     9     6     1     8     3     6     7     9     4     7     1     3     6     9     9     5    o     9  .  .    5  .  .  .  .  .  .     8  .    7    5     0     4     1     6     1     3     7     1     8     2     3     8     2     6     1     9    6  .  .  .  .  .     1    2  .  .     0     3     1    6  .  .     0  .  .  .  .  .     3    0    1  .  .  .  .    8  .     6  .    p     S     1    1    8    9    7    8    7    1    8    7    1    1    1    7    6    1    8    8    6    1    9    1    1    4    8    5    8    4    9

    d    e    u     E    i     L   n    t     B   n     A   o     T    C

  -      2     *      1     *    d     *       *     h     *    n     *    g     2   -   -     *       *    *        1       *     2     *      1      2        2     h     *     *          2    u   n   u    r     k     *   -     1      2   -     *      3     *     *    g     2    n       *     1  -     2  -    n      1    o     *    e    t    c      1     p    o      2    u    k     k    w      1   -     2      r    *    w     *      1      1      1      2    r    g    c    t     f     p    n    t    t    w    v    c      2    o      2    a    u    t    o      1     /     *    u    h    u     f    a    d    b    t     k    o     2    a    o    p    n    u   u    r    b    o    n    n   w    t     d    a     1    u    o    o    c    u    n    l    u    o    i    u   o    o    o     d     b    o     i    o    u    n    o    o    n     h    a    w    e    e    e     d    e    e    e    e    e    o    b    e    v    k    e    o    e    n    v    s     i    e    e    a     d    t     l    t    v    l     l    e    s     k    o     d     l    t     i    s    t     l     l    m    t    v    a    u   o    m    m   e    m    c    k    v    i    v    o    t    u   o    i     V    a    t     l    e    o    r    o    l    o    a    k    o    l    e    r    o    l     i     i    a    e    i    a    t    o    o    t     i     i     P    w   g    h    w   m    f    s    r    m    p    t    g    g    h    s    m   c    p    t    s    s    f    c    s    c    f    g    g    g

PHRASAL VERBS IN AMERICAN AND BRITISH ENGLISH

687

 

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688

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