Using collocation clusters to detect and correct English L2 learners' collocation errors. Issue 3 (12th May 2021)

• Record Type: Journal Article
• Authors: Huang, Ping-Yu; Tsao, Nai-Lung
• Is Part Of: Computer assisted language learning. Volume 34:Issue 3(2021)
• Page Start: 270
• Page End: 296
• Publication Date: 2021-05-12
• Subjects: Collocation cluster -- collocation error detection -- collocation error correction -- digital reference tool; Language and languages -- Computer-assisted instruction -- Periodicals; 418.00285
• DOI: 10.1080/09588221.2019.1607880
• Languages: English
• ISSNs: 0958-8221

Abstract

Abstract: In this article, we describe an online English collocation explorer developed to help English L2 learners produce correct and appropriate collocations. Our tool, which is able to visually represent relevant correct/incorrect collocations on a single webpage, was designed based on the notions of collocation clusters and intercollocability proposed by Cowie and Howarth. As they pointed out, in a collocation cluster L2 learners generally cannot distinguish true collocations (e.g., tell truth, state truth, and state fact ) from impossible combinations (e.g., * say fact and * say truth ). Accordingly, our tool applies natural language processing techniques to construct collocation clusters to enable learners to easily differentiate between correct and incorrect pairs. Relying on data from a reference corpus, our system instantaneously processes the collocability of users' target combination (verb–noun or adj–noun) and all other relevant words and presents true/false collocations that L2 learners should master/avoid. To assess our tool, we investigated its performance in detecting and correcting learners' V–N and A–N errors, with results comparable to those of most previous studies. Piloted using a sample of 13 intermediate- or upper-intermediate level English as a foreign language learners, our tool was found to help them self-correct their collocation errors effectively. Compared with similar tools or approaches, our tool requires much less data resources, but still demonstrates a remarkable capability to detect/correct errors and generate useful collocational knowledge in English.