Automatic generation of high quality CCGBanks for ParSER domain adaptation

Masashi Yoshikawa, Hiroshi Noji, Koji Mineshima, Daisuke Bekki

研究成果: Conference contribution

抄録

We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. We conduct extensive parsing experiments with detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and (2) question sentences, we create experimental datasets of (3) speech conversation and (4) math problems. When applied to the proposed method, an off-the-shelf CCG parser shows significant performance gains, improving from 90.7% to 96.6% on speech conversation, and from 88.5% to 96.8% on math problems.

本文言語English
ホスト出版物のタイトルACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
出版社Association for Computational Linguistics (ACL)
ページ129-139
ページ数11
ISBN(電子版)9781950737482
出版ステータスPublished - 2020
外部発表はい
イベント57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 - Florence, Italy
継続期間: 2019 7 282019 8 2

出版物シリーズ

名前ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics, ACL 2019
国/地域Italy
CityFlorence
Period19/7/2819/8/2

ASJC Scopus subject areas

  • 言語および言語学
  • コンピュータ サイエンス(全般)
  • 言語学および言語

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