| { |
| "paper_id": "Y00-1028", |
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| "date_generated": "2023-01-19T13:45:12.075167Z" |
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| "title": "Collocation Deficiency in a Learner Corpus of English: from an overuse perspective", |
| "authors": [ |
| { |
| "first": "Rebecca", |
| "middle": [], |
| "last": "Hsue", |
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| "affiliation": { |
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| "institution": "National Sun Yat-sen University", |
| "location": { |
| "settlement": "Kaohsiung", |
| "country": "Taiwan, R.O.C" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Hueh", |
| "middle": [], |
| "last": "Shih", |
| "suffix": "", |
| "affiliation": { |
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| "institution": "National Sun Yat-sen University", |
| "location": { |
| "settlement": "Kaohsiung", |
| "country": "Taiwan, R.O.C" |
| } |
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| "email": "" |
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| "abstract": "Collocational deficiency is a pervasive phenomenon in learner English. Language learners often fail to choose the correct combination of two or more words due to their unawareness of collocational properties in vocabulary. They are apt to adopt lexical simplification strategies such as using a synonymous or Ll-influenced expression. This paper presents a corpus-based study on the collocational deficiency of Taiwanese learners of English. The work utilizes two pre-tagged corpora, Taiwanese Learner Corpus of English and British National Corpus, to examine the learner's use of collocations over a set of", |
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| "text": "Collocational deficiency is a pervasive phenomenon in learner English. Language learners often fail to choose the correct combination of two or more words due to their unawareness of collocational properties in vocabulary. They are apt to adopt lexical simplification strategies such as using a synonymous or Ll-influenced expression. This paper presents a corpus-based study on the collocational deficiency of Taiwanese learners of English. The work utilizes two pre-tagged corpora, Taiwanese Learner Corpus of English and British National Corpus, to examine the learner's use of collocations over a set of", |
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| "text": "Corpus Linguistics studies language features based on large databases of authentic language samples stored on computer. Because its automated quantitative analysis provides novel and refreshing insights into real language use [1] , the corpus-based approach has rapidly spread into many language-related research. SLA (Second Language Acquisition) and EFL (English as Foreign Language) specialists, with no exception, consider large databases of learner English a useful resource for them to gain concrete evidence and a wider perspective on learners' inter-language acquired during the process of language learning. Therefore, computer learner corpora (CLCs) with various mother tongue backgrounds have been subsequently constructed [2] .", |
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| "text": "[1]", |
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| "section": "Introduction", |
| "sec_num": "1." |
| }, |
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| "text": "With gradual availability of CLC and the awareness of its potential, a wide range of CLCresearch starts to boom. The research often involves comparisons between inter-language that learners possess and native language on various linguistic features. For instance, the frequency distributions of most commonly-used words in a native and seven eastern European learner corpora are compared on different parts-of-speech categories [3] ; the use of complement clauses in four learner corpora as contrasted with their native counterparts [4] is studied; the use of adverbial connectors by Swedish learners in comparison with the natives' is examined [5] . This kind of cross-language approach helps SLA and EFL specialists find out what linguistic features the language learners are apt to overuse/underuse, what are the particular areas of language behavior shared by learners from different backgrounds, and to what extent these phenomena appear in learner English. The quantitative information as such guides the researchers to carry out insightful qualitative analysis.", |
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| "start": 428, |
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| "text": "[3]", |
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| "section": "Introduction", |
| "sec_num": "1." |
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| "text": "While the CLC-research focuses on the core aspects of learners' lexis, grammar and discourse, collocation deficiency, a pervasive phenomenon in every learner corpus, remains intact. Collocation is the habitual co-occurrence of two or more words in a text, and is an important feature for vocabulary learning. However, due to the traditional grammar-based EFL pedagogy, the collocational property in relation to each item of vocabulary has been neglected in EFL class [6] [7] . When learners encounter a collocation problem, they tend to resort to one of the strategies of lexical simplification: synonym, avoidance, transfer and paraphrasing [8] . Table 1 [6] , can be viewed as a direct consequence of the unawareness of collocational restrictions between lexical items. Avoidance strategy is adopted when learners avoid a correct lexical item in favor of another, and thus alters the meaning of collocation. The use of transfer creates L 1-influenced collocational errors and is the result of learners' working hypothesis that there is one to one correspondence between Ll and L2 [9] .", |
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| "text": "Since learners tend to choose whatever word is easily and readily retrievable in their minds when putting the strategies in practice, certain commonly-used synonymous words are apt to be overused in learner English and more data from other universities will be collected in the next couple of years to make the corpus more representative. The BNC contains modern British English and is a unique collaboration between three major U.K. dictionary publishers, two universities, and the British Library [10] . The work here utilizes mainly its subset of 1 million words (from BNC Sampler written text), but its complete set was consulted in the situation where more data is needed as in Section 3.4 and 3.4 .", |
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| "section": "Introduction", |
| "sec_num": "1." |
| }, |
| { |
| "text": "The corpora are lemmatized and part-of-speech tagged with the TOSCA tagger [11] .", |
| "cite_spans": [ |
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| "start": 75, |
| "end": 79, |
| "text": "[11]", |
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| "eq_spans": [], |
| "section": "Analysis Tools: TOSCA and CCS", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "TOSCA is a stochastic tagger, supplemented with a rule-based component which tries to correct observed systematic errors of the statistical components. TOSCA also gives each word form its lemma (basic form). For instance, word forms such as takes, took, taken, and taking have the same lemma take. This function facilitates the collocation analysis under the same lemma. TOSCA operates with a lexicon, which currently contains about 160,000 lemma-tag pairs, covering about 90,000 lemmas. The TOSCA-ICLE tagset contains 270 different tags within 16 major word classes. For simplicity, only the major word classes are considered in the current study.", |
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| "section": "Analysis Tools: TOSCA and CCS", |
| "sec_num": "2.2" |
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| "text": "Corpus analysis tools such as WordSmith [12] and Qwick [13] are very popular and useful software for concordance and simple collocation search. However, they only take raw text as input, and thus fail to perform more sophisticated functions, such as the search of collocations in terms of their lemmas and parts-of-speech. To facilitate analysis required in this work, software for sophisticated collocation searching, Corpus Collocation Searcher (CCS), is specially developed. CCS takes .TOSCA tagged data as input and enables users to enter either a word form, lemma or even part-of-speech as a search keyword. It provides the same mechanism for collocate specification. For instance, users are able to search the noun collocates immediately following the keywords great, greater and greatest by specifying the lemma form of the keyword, great, the part-of-speech of the collocates, NOUN, and the location of the collocates.", |
| "cite_spans": [ |
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| "start": 40, |
| "end": 44, |
| "text": "[12]", |
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| "start": 55, |
| "end": 59, |
| "text": "[13]", |
| "ref_id": "BIBREF12" |
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| "section": "Analysis Tools: TOSCA and CCS", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "The frequencies of big, large and great are calculated from both of the corpora. Figure 1 indicates the frequencies (per million words) for each of these synonymous words in TLCE and BNC. As shown in the figure, big and large both show a considerable discrepancy in the number of their occurrences between the two corpora, while great doesn't. The frequency of big in the learner corpus is almost double the number of occurrences in its native counterpart, whereas large in TLCE appears only one fifth of the number of occurrences in BNC. Like many other adjectives, big has both attributive (big + noun) and predicative (noun + be + big) functions. Since the attributive big (here assumed to be immediately followed by a noun) takes up majority of the occurrences (nearly 70%) in the learner corpus, and the CCS tool so far doesn't provide a function to locate the subject noun of the predicative big, the following experiments are based on its attributive function only.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 81, |
| "end": 89, |
| "text": "Figure 1", |
| "ref_id": "FIGREF1" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Frequency Distribution of the Synonymous Words", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "The study in [1] showed that the vast majority of occurrences of big in native corpora are used to refer to physical size of objects. Thus, this experiment was carried out to examine the properties of nouns that big collocates in TLCE. Figure 2 shows the ratio of abstract to concrete collocates in both corpora. As shown, nearly 70% of the occurrences of big in BNC are used to refer to concrete objects, but only 55% in TLCE. In other words, the Taiwanese learners use the word big much more often than native speakers do when describing abstract concepts. In contrast to 30% in BNC, the collocations of big and the referred abstract concepts take up 45% of total number of the occurrences in TLCE. As the use of big to refer to physical size is less problematic, next experiment are carried out to examine the use of its abstract noun collocates. ", |
| "cite_spans": [ |
| { |
| "start": 13, |
| "end": 16, |
| "text": "[1]", |
| "ref_id": "BIBREF0" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 236, |
| "end": 244, |
| "text": "Figure 2", |
| "ref_id": "FIGREF2" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Abstract vs. Concrete Collocated Nouns", |
| "sec_num": "3.2" |
| }, |
| { |
| "text": "To further study the abstract collocates of big, it is desirable to find out what these abstract nouns are and how the Taiwanese learners use them differently from native speakers.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distribution of Abstract Noun Collocates", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "Three kinds of data are concerned here: (1) abstract nouns (N) which collocate with big more than once in TLCE, (2) freq(big, N)-the frequency of co-occurrences of big and N, and 3the ratio of freq(big, N) to freq(N). Table 2 shows the comparisons of these figures in both TLCE and BNC. As some collocations of big in TLCE do not occur in the subset of BNC, a complete BNC of 100 million words is consulted for comparison. It is observed from learner's writing in TLCE that the learners overuse certain collocations to deliver vague ideas in the situations where more specific_ meanings are acquired; they also apply some collocations which are easily retrievable in their mind to the cases where more concise terms are usually expressed. Two examples given below explain these phenomena: As can be seen in (a), \"big problem\" conveys a vague meaning which is realized later in the writing as \"financial problem\", and the three-word phrase, \"a big trouble\", in (b) can be replaced by a concise expression, \" very troublesome\".", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 218, |
| "end": 225, |
| "text": "Table 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Distribution of Abstract Noun Collocates", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "Some collocations in TLCE, pertaining to big, large or great, do not appear in BNC and are considered to be problematic. Table 3 are due to the strategies of lexical simplification that the Taiwanese learners put to use in their writing. When describing the first four abstract nouns, Taiwanese learner directly translate the concept of \"big\", which goes naturally with these nouns in their mother tongue language, into English. This results in the problematic collocations: *big turn, *big wind, *big slam and *big nature, rather than the correct use of sharp turn, strong wind, loud slam and Nature in BNC. The misuse of *large regret, *large trouble and *great jealousy can be viewed as the application of the synonym strategy.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 121, |
| "end": 128, |
| "text": "Table 3", |
| "ref_id": "TABREF2" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Problematic Collocations", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "This paper presents a corpus-based study on examining the collocational deficiency of Taiwanese learners of English from an overuse perspective. The experimental results give the followings findings. Firstly, among the synonymous word, big, large and great, the Taiwanese learners overuse big significantly when it is used to refer to abstract concepts. Secondly, the phenomenon that certain collocations with big in TCLE appear far more frequently than in BNC can be explained by the observations that the learners use big to convey vague ideas when more specific meanings should be expressed and that they are apt to apply easily retrievable collocations to the cases where more concise expressions are preferred. Finally, transfer and synonym are the main simplification strategies that the Taiwanese learners adopted when they encounter a collocational problem.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Further Work", |
| "sec_num": "4." |
| }, |
| { |
| "text": "In this work, the predicate function of big was not examined due to the difficulty of locating the precedent subject using the current CCS tool. In the future, CCS will be augmented to facilitate the investigation on this part for a thorough study.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Further Work", |
| "sec_num": "4." |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "The author would like to thank her colleagues for collecting student's compositions and Dr.Ching-yuan Tsai for his valuable discussion. This work is supported by the National Science Council, Taiwan, R.O.C.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgements", |
| "sec_num": null |
| } |
| ], |
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| "raw_text": "Aarts, J., H. Barkema, and N. Oostdijk, The TOSCA-ICLE Tagset Software and Tagging Manual, . 1997, The Department of Language and Speech, University of Nijmegen, The Netherlands.", |
| "links": null |
| }, |
| "BIBREF12": { |
| "ref_id": "b12", |
| "title": "Language Independent Statistical Software for Corpus Exploration", |
| "authors": [ |
| { |
| "first": "J", |
| "middle": [], |
| "last": "Sinclair", |
| "suffix": "" |
| } |
| ], |
| "year": 1998, |
| "venue": "Computers and the Humanities", |
| "volume": "31", |
| "issue": "3", |
| "pages": "229--255", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Sinclair, J., et al., Language Independent Statistical Software for Corpus Exploration. Computers and the Humanities, 1998. 31(3): p. 229-255.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "num": null, |
| "uris": null, |
| "text": ". The work in this paper is designed to examine the collocational deficiency of Taiwanese learners of English from an overuse perspective, and find out in what context the problematic collocations occur. The work investigates this issue over a set of synonymous words, big, large and great. It uses two pre-tagged corpora, Taiwanese Learner corpus of English (TLCE) and British National Corpus (BNC), which will be stated subsequently in Section 2.1. The computer-assisted tools for tagging and lemmatizing of the corpora and for quantitative analysis will be described in Section 2.2. A series of experiments and the results are shown and discussed in Section 3. Concluding remarks are made in Section 4. 2. Methodology 2.1 Corpora: TLCE and BNC As stated in the introduction, CLC-research often compares non-native data with native data in order to reveal the overuse and/or underuse phenomena in a learner corpus. In this work, the Taiwanese Learner Corpus of English (TLCE) of 286,600 words is under investigation and the British National Corpus (BNC) of 100 millions words is used for comparison. TLCE is a growing corpus of English compositions and weekly journals written mainly by college English majors in Taiwan. They are freshmen, sophomores and juniors of age ranging from 19 to 22. The current data are from Sun Yat-sen and Chi-nan universities,", |
| "type_str": "figure" |
| }, |
| "FIGREF1": { |
| "num": null, |
| "uris": null, |
| "text": "Frequency Distribution of the words concerned the introduction, certain commonly-used simple words are apt to be overused by language learners in the course of lexical simplification (synonym, transfer and avoidance) due to their easy retrievability. To further investigate the phenomenon of overuse in relation to learner collocation deficiency, it is necessary to examine in great detail the collocational property of big in the learner corpus. As to the underuse of large, it is not in the focus of the present study.", |
| "type_str": "figure" |
| }, |
| "FIGREF2": { |
| "num": null, |
| "uris": null, |
| "text": "Distribution of Abstract and Concrete Nouns which collocate with 'big'", |
| "type_str": "figure" |
| }, |
| "FIGREF3": { |
| "num": null, |
| "uris": null, |
| "text": "Distribution of highly collocated nounsAs shown in the table, the collocation of big problem gives the highest co-occurrence frequency with big in TLCE, and the frequency ratio for the use of big problem over problem is 3.8%. However, the same collocation appearing in BNC of 1 million and BNC of 100 million words only shows the ratios of 0.6% and 0.4% respectively. This large discrepancy in the ratio of using certain collocation between learner and native English spreads across other high frequency collocates, except the collocation big turn, which never appears in the native corpus and is treated as a problematic collocation in the next experiment.", |
| "type_str": "figure" |
| }, |
| "TABREF0": { |
| "content": "<table><tr><td colspan=\"2\">of English (* is used to indicate collocational errors):</td><td/></tr><tr><td>Problematic Collocations</td><td>Correct Collocations</td><td>Strategies Applied</td></tr><tr><td>* rules are loose</td><td>Rules are lenient</td><td>Synonym</td></tr><tr><td>* great drinker</td><td>Heavy drinker</td><td>Avoidance</td></tr><tr><td>* age layers</td><td>Age groups</td><td>Transfer</td></tr><tr><td>Inconvenience in moving</td><td>Transport difficulty</td><td>Paraphrasing</td></tr><tr><td colspan=\"3\">Table 1: Examples of lexical simplification by Taiwanese Learners of English</td></tr><tr><td colspan=\"3\">Apart from paraphrasing which is considered a good strategy in L2 (second language)</td></tr><tr><td colspan=\"3\">communication, the other three uses result in unacceptable collocational mistakes in language</td></tr><tr><td colspan=\"3\">learning. The most commonly used strategy, synonym taking up 38% of total colloational</td></tr><tr><td>errors</td><td/><td/></tr></table>", |
| "text": "lists the examples of the four strategies used by Taiwanese learners", |
| "html": null, |
| "type_str": "table", |
| "num": null |
| }, |
| "TABREF1": { |
| "content": "<table/>", |
| "text": "\"... but I have a big, big, big problem, that is, that I don't have a camera... ... However, a camera is really very expensive. ... (b) \"... it will be a_big_trauble to move all my things to another place. ..", |
| "html": null, |
| "type_str": "table", |
| "num": null |
| }, |
| "TABREF2": { |
| "content": "<table><tr><td>Abstract Noun</td><td colspan=\"2\">Misused Collocates in TLCE Common Collocates in BNC</td></tr><tr><td>turn</td><td>big</td><td>sharp</td></tr><tr><td>wind</td><td>big</td><td>strong</td></tr><tr><td>slam</td><td>big</td><td>loud</td></tr><tr><td>nature</td><td>big</td><td>-</td></tr><tr><td>regret</td><td>large</td><td>great, big</td></tr><tr><td>trouble</td><td>large</td><td>great, big</td></tr><tr><td>'ealous</td><td>treat</td><td>intense acute</td></tr><tr><td/><td colspan=\"2\">Table 3. Misuse of Collocates in TLCE</td></tr><tr><td colspan=\"2\">The collocation errors listed in</td><td/></tr></table>", |
| "text": "lists those collocations in question, with misused adjective collocates in TLCE and common collocates in BNC.", |
| "html": null, |
| "type_str": "table", |
| "num": null |
| } |
| } |
| } |
| } |
