Automatic generation of high quality CCGbanks for parser domain adaptation

Masashi Yoshikawa, Hiroshi Noji, Koji Mineshima, Daisuke Bekki

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2019 Jun 5
Externally publishedYes

ASJC Scopus subject areas

  • General

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