以半自動化擷取方法探究大型學習者語料庫之搭配詞錯誤

Translated title of the contribution: A Semi-automatic Error Retrieval Method for Uncovering Collocation Errors from a Large Learner Corpus

Christine Ting Yu Yang, Howard Hao Jan Chen, Chen Yu Liu, Yu Cheng Liu

Research output: Contribution to journalArticle

Abstract

Previous studies on ESL/EFL learners’ verb-noun (V-N) miscollocations have shed some light on common miscollocation types and possible causes. However, barriers to further understanding of learners’ difficulties still exist, such as the limited amount of learner data generated from small corpora and the labor-intensive process of manually retrieving collocational errors. To provide researchers with a more efficient retrieval method, this study proposed the use of the Sketch-Diff function in the Sketch Engine (SKE) platform to semi-automatically retrieve collocation errors in large learner corpora. To test the feasibility of this semi-automatic retrieval method, a 7.4-million-word EFL learner corpus was investigated with Sketch-Diff, and 4541 tokens of common miscollocations were identified. Analysis of these miscollocations revealed that most errors were verb-based and often caused by negative transfer from the learners’ L1, undergeneralization (e.g., ignorance of L2 syntactic rules), and approximation (e.g., the misuse of near-synonyms, hyper-/hyponyms, antonyms, and lexemes with similar sound/form). This study demonstrates that using Sketch-Diff to retrieve V-N miscollocations from a large learner corpus is both feasible and efficient. This method can be applied to other languages to further deepen our understanding of L2 learners’ difficulties in collocation acquisition.

Translated title of the contributionA Semi-automatic Error Retrieval Method for Uncovering Collocation Errors from a Large Learner Corpus
Original languageChinese
JournalEnglish Teaching and Learning
DOIs
Publication statusAccepted/In press - 2019 Jan 1
Externally publishedYes

ASJC Scopus subject areas

  • Education
  • Linguistics and Language

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