Knowledge-enriched two-layered attention network for sentiment analysis

Abhishek Kumar, Daisuke Kawahara, Sadao Kurohashi

研究成果: Conference contribution

15 被引用数 (Scopus)

抄録

We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis. The novel two-layered attention network takes advantage of the external knowledge bases to improve the sentiment prediction. It uses the Knowledge Graph Embedding generated using the Word- Net. We build our model by combining the two-layered attention network with the supervised model based on Support Vector Regression using a Multilayer Perceptron network for sentiment analysis. We evaluate our model on the benchmark dataset of SemEval 2017 Task 5. Experimental results show that the proposed model surpasses the top system of SemEval 2017 Task 5. The model performs significantly better by improving the state-of-the-art system at SemEval 2017 Task 5 by 1.7 and 3.7 points for sub-tracks 1 and 2 respectively.

本文言語English
ホスト出版物のタイトルShort Papers
出版社Association for Computational Linguistics (ACL)
ページ253-258
ページ数6
ISBN(電子版)9781948087292
出版ステータスPublished - 2018
外部発表はい
イベント2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States
継続期間: 2018 6 12018 6 6

出版物シリーズ

名前NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
2

Conference

Conference2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
国/地域United States
CityNew Orleans
Period18/6/118/6/6

ASJC Scopus subject areas

  • 言語学および言語
  • 言語および言語学
  • コンピュータ サイエンスの応用

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