| 研究課題/領域番号 |
13F03041
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| 研究機関 | 東京大学 |
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研究代表者 |
鶴岡 慶雅 東京大学, 大学院工学系研究科, 准教授
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| 研究分担者 |
STENETORP Pontus 東京大学, 大学院工学系研究科, 外国人特別研究員
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| キーワード | 深層学習 / 句の表現 / ジョイントモデリング / 意味解析 / 構文解析 / ベクトル空間モデル |
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| 研究概要 |
Over the last year we have been working on the problem of creating representations of phrases. For this we have followed two approaches. First, a more traditional approach of creating the representation while analysing the syntactic structure of the sentence (Dependency Parsing). Work which Dr. Stenetorp recently presented. Secondly, a more unconventional approach of avoiding any reliance on syntactic structure and not just learn the representation but also the structure jointly. The latter is work Dr. Stenetorp started during his visit to Stanford earlier this year and we so far have promising results, being able to achieve performance on-par with models that utilise manually annotated structures. What the models have in common is that they draw upon recent advances in Deep Learning and allow us to learn a representation jointly with an end task such as semantic category disambiguation or phrase normalisation.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We have made good progress towards the goal of classifying phrases and assigning them an appropriate semantic category. Both of the two approaches we have worked on are showing promise and the joint Dependency Parsing approach has been picked up by several other external research groups, among them the NLP group at the Nara Institute of Science and Technology.
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| 今後の研究の推進方策 |
We are currently working on two publications, one extension of Dr Stenetorp's work from last year on jointly composing a phrase representation while performing Dependency Parsing and also wrapping up the collaborative work with Stanford. Once these publications have been cleared we expect that we can move on working with both styles of phrasal representations and use them as the basis for our joint phrase normalisation system.
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