A Tentative Corpus-Based Study of Collocations Acquisition by Chinese English Language Learners/UNE ETUDE TENTATIVE DE RECUEIL-BASEE SUR L'APPRENTISSAGE DES ACCORDS PAR LES APPRENANTS CHIOIS D'ANGLAIS
Bibliographic record
Abstract
Abstract: Though collocations have drawn much attention in the field of language acquisition, yet difficulties with them have not been investigated in much detail. This paper reports on a corpus-based exploratory study that analyzes the mistakes learners made when they produced English collocations. The study shows that not only beginners but also advanced learners have difficulties in choosing the right collocates and the difficulties that learners of different levels have are more or less the same. The biggest challenge for them is to choose the appropriate verbs. The L1 influence on the production of L2 collocations exists at every stage of learning though it varies with the learners' L2 competence. Based on this study, a corpus-based approach is advanced in the end to cope with the difficulties in the acquisition of L2 collocations. Key words: Collocation, second language acquisition, corpus-based, CLEC Resume : Ce document fait un bilan sur une etude explorateur de recueil-basee qui fait une analyse des erreurs commis par les apprenants au cas des accords. Cette etude montre que non seulement les debutants mais aussi les apprenants du niveau avance ont du mal a choisir un bon terme d'accord et que les erreurs y reviennent au meme pour tout niveau. Le plus grand defi pour eux est de choisir le mot juste. Le fait que la langue 1 inflence sur la production du choix d'accord existe au niveau quel que ce soit malgre la variation du niveau de langue 2 des apprenants. Base sur cette etude, une approche recueil-basee est engagee a la fin pour traiter ce probleme existant dans l'apprentissage de l'accord en Langue 2. Mots clefs: Accord , apprentissage de la langue secondaire, recueil-basee , CLEC ((ProQuest-CSA LLC: ... denotes non-USASCII text omitted.) 1. INTRODUCTION Although a corpus-based approach to SLA research and foreign language teaching is still in its infancy, there has been a growing interest in this new field. There are several reasons for this. Firstly, it is the computer that has introduced incredible speed, total accountability, accurate replicability, statistical reliability and the ability to handle huge amounts of data (Kennedy 2000: 5). Secondly, a growing awareness of the usefulness of quantitative data provides major impetuses to the re-adoption of the corpus-based language study3 as a methodology in linguistics (McEnery and Wilson 1996: 18). Many SLA researchers have found it very difficult to simply follow what theoretical linguists or psycholinguists say about an L1 acquisition model and check if the same abstract linguistic principle is still applicable in L2 learning. More and more researchers now prefer to look at real language performance data instead of relying too much on intuitive or introspective data. Thirdly, it is widely accepted that in modern language classroom the teacher should act as a research facilitator rather than the more traditional imparter of knowledge. Under such a student-centered teaching background, corpus-based adaptive learning has gained much attention ... 2001). Recently there has been a growing awareness that it is necessary to investigate learner language by collecting a large amount of learner performance data on computer. The term 'learner's corpus' was first used for Longman's learners' dictionaries, in which the information on EFL learners' common mistakes was provided. A project called ICLE (International Corpus of Learner English) was launched as a part of ICE (International Corpus of English) project in 1990. Now more than a dozen of projects constructing learner corpora have been underway around the world, (see the web site: http://www.lancs.ac.uk/postgrad/tono/) In China, an available learner corpus is CLEC (Chinese English Learner Corpus), which was built under the lead of Professor Gui Shichun and Professor Yang Huizhong. Some researches on language acquisition have focused on the phenomenon of collocation. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".