| Summary: | 博士 === 國立清華大學 === 資訊系統與應用研究所 === 98 === The concept of collocation has been widely discussed in the field of language teaching for decades. It has been shown that collocation is important in helping language learners achieve native-like fluency. In the field of English for academic purpose, there are also more and more researchers recognizing this important feature in academic writing. It is often argued that collocation can influence the effectiveness of a piece of writing and the lack of such knowledge might cause cumulative loss of precision.
Previous research indicates effective collocation acquisition needs learners’ awareness while they learn vocabulary. However, this strategy might not be easy to apply in a real-life classroom. We not only need to equip language instructors with rich knowledge of collocation but also need to help instructors correct students collocation errors, which is labor intensive and time consuming. In addition, to automate collocation suggestion via language technology requires considerable efforts. A proper collocation suggestion might involve knowing the correct semantic as well as pragmatic usages. It is thus still an unresolved issue in need of particular attention.
In our thesis, we prove the feasibility of using a machine learning method to build a writing assistant which is aimed at automatically prompting learners with collocation suggestions in academic writing. Given an input sentence, which requires collocation suggestions, we build a data-driven classifier and treat the outcome of the classification as suggested substitutions in question. Moreover, for a robust classifier, feature selection is the key component. We make use of the target’s contextual linguistic clues to elicit the most relevant suggestions from the reference corpus of scholarly texts.
We carried out an experiment focusing on one of the major types of collocation problems, verb-noun collocations. The proposed classifier along with contextual information can satisfactorily return suggestions with the best hit rank in the experiment. Our framework of computer-assisted academic writing can facilitate learner-writers’ collocation uses and help to transfer that knowledge to their future writing.
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