Mining Words in the Minds of Second Language Learners for Learner-specific Word Difficulty
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- Ehara Yo
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology
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- Sato Issei
- Graduate School of Information Science and Technology, The University of Tokyo
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- Oiwa Hidekazu
- Recruit Institute of Technology
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- Nakagawa Hiroshi
- Information Technology Center, The University of Tokyo
Abstract
<p>While there have been many studies on measuring the size of learners' vocabulary or the vocabulary they should learn, there have been few studies on what kind of words learners think that they know. Therefore, we investigated theoretically and practically important models for predicting second language learners' vocabulary and propose another model for this vocabulary prediction task. With the current models, the same word difficulty measure is shared by all learners. This is unrealistic because some learners have special interests. A learner interested in music may know special music-related terms regardless of their difficulty. To solve this problem, our model can define a learner-specific word difficulty measure. Our model is also an extension of these current models in the sense that these models are special cases of our model. In a qualitative evaluation, we defined a measure for how learner-specific a word is. Interestingly, the word with the highest learner-specificity was “twitter.” Although “twitter” is a difficult English word, some low-ability learners presumably knew this word through the famous micro-blogging service. Our qualitative evaluation successfully extracted such interesting and suggestive examples. Our model achieved an accuracy competitive with the current models.</p>
Journal
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- Journal of Information Processing
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Journal of Information Processing 26 (0), 267-275, 2018
Information Processing Society of Japan
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Details 詳細情報について
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- CRID
- 1390282680271622144
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- NII Article ID
- 130006507582
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- ISSN
- 18826652
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed