| Summary: | 碩士 === 國立清華大學 === 資訊系統與應用研究所 === 94 === There has been increasing interest in exploiting Natural Language Processing (NLP) technology in Computer Assisted Language Learning (CALL). Advances have been made in automatic rating of essays in standardized tests. There is also a need for automatic programs that generate test items that, after minor post-editing, are applicable in self-paced learning and low-stakes testing situations. This paper presents a novel NLP-based approach to facilitate the reading process of self-paced online learning, and to assist the semi-automatic generation of test items for reading comprehension tests (RCTs).
The method involves identifying key words and key sentences, disambiguating word sense of the key words, paraphrasing part of the sentences, displaying disambiguated keyword definitions and paraphrased verb phrase alternatives. For that, senses of words are transformed into a set of sense-related queries combined to be with context information to collect disambiguation information or paraphrase data from the Web. We implement the proposed method based on the concept of Web-as-Corpus (WAC) for the semantic processing of word sense disambiguation and paraphrasing. Evaluation on a set of official TOEFL reading passages suggests that such a procedure is effective in terms of time, labor, and quality. Our methodology clearly provides potential for exploiting the web-based data, turning authentic texts into enriched reading materials, and assisting the generation of effective test items for reading comprehension tests.
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