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An Automatic Collocation Writing Assistant for Taiwanese EFL Learners: A Case of Corpus-Based NLP Technology

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Computer Assisted Language Learning Volume 21, Number 3, ISSN 0958-8221


Previous work in the literature reveals that EFL learners were deficient in collocations that are a hallmark of near native fluency in learner's writing. Among different types of collocations, the verb-noun (V-N) one was found to be particularly difficult to master, and learners' first language was also found to heavily influence their collocation production. In this paper, we develop an online collocation aid for EFL writers in Taiwan, aiming at detecting and correcting of learners' miscollocations attributable to L1 interference. Relevant correct collocation as feedback messages is suggested according to the translation equivalents between learner's L1 and L2. The system utilizes natural language processing (NLP) techniques to segment sentences in order to extract V-N collocations in given texts, and to derive a list of candidate English verbs that share the same Chinese translations via consulting electronic bilingual dictionaries. After combining nouns with these derived candidate verbs as V-N pairs, the system makes use of a reference corpus to exclude the inappropriate V-N pairs and single out the proper collocations. The system can effectively pinpoint the miscollocations and provide the learner with adequate collocations that the learner intends to write but misuses. It is hoped that this online assistant can facilitate EFL learner-writers' collocation use and help them transfer this essential knowledge to their future writing. (Contains 10 tables, 2 figures and 6 notes.)


Chang, Y.C., Chang, J.S., Chen, H.J. & Liou, H.C. (2008). An Automatic Collocation Writing Assistant for Taiwanese EFL Learners: A Case of Corpus-Based NLP Technology. Computer Assisted Language Learning, 21(3), 283-299. Retrieved October 21, 2020 from .

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