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A Semi-automatic Error Retrieval Method for Uncovering Collocation Errors from a Large Learner Corpus

  • Christine Ting-Yu Yang
  • Howard Hao-Jan ChenEmail author
  • Chen-Yu Liu
  • Yu-Cheng Liu
Original Paper

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.

Keywords

Collocation Error extraction method Learner corpus Error analysis 

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

中文摘要

過去針對以英語為二語/外語的學習者的動詞—名詞(動-名)錯誤搭配研究, 已揭示常見之錯誤類型與可能成因。然而, 此類研究多採用較為耗費人力之搭配詞檢索方式, 來探究小型語料庫之錯誤情形, 造成學者難以將此類擷取方法應用至大型語料庫。為提供研究者更有效率之搭配詞錯誤擷取方法, 本文提倡使用Sketch Engine語料處理平台的Sketch-Diff功能, 以半自動化方式自大型學習者語料庫中擷取搭配詞錯誤。為測試此半自動化擷取方式之可行性, 本文以Sketch-Diff檢驗一座七百四十萬字的學習者語料庫中的錯誤搭配錯誤情形, 並判別出4,541筆常見之動─名錯誤搭配詞。分析結果指出多數錯誤為動詞之誤用, 而歸咎其成因則多為學習者之母語負遷移、生成不足(如:忽略二/外語句法規則)以及相近表達誤用(如:近義詞誤用、上/下位詞誤用、反義詞誤用以及形/音近詞誤用)。本文研究結果表明Sketch-Diff功能可有效以半自動化方式自大型學習者語料庫擷取動—名搭配詞錯誤, 並建議應用此研究方法至其他語言, 以進一步加深對二/外語學習者搭配詞習得困難之理解。

關鍵詞

搭配詞 錯誤擷取方法 學習者語料庫 錯誤分析 

Notes

Funding Information

This work was financially supported by the “Chinese Language and Technology Center” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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Copyright information

© National Taiwan Normal University 2019

Authors and Affiliations

  • Christine Ting-Yu Yang
    • 1
  • Howard Hao-Jan Chen
    • 1
    Email author
  • Chen-Yu Liu
    • 1
  • Yu-Cheng Liu
    • 2
  1. 1.English Department, National Taiwan Normal UniversityTaipei CityTaiwan
  2. 2.Taipei First Girls High SchoolTaipei CityTaiwan

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