Dependency parse reranking with rich subtree features

Mo Shen*, Daisuke Kawahara, Sadao Kurohashi

*この研究の対応する著者

研究成果: Article査読

4 被引用数 (Scopus)

抄録

In pursuing machine understanding of human language, highly accurate syntactic analysis is a crucial step. In this work, we focus on dependency grammar, which models syntax by encoding transparent predicate-argument structures. Recent advances in dependency parsing have shown that employing higherorder subtree structures in graph-based parsers can substantially improve the parsing accuracy. However, the inefficiency of this approach increases with the order of the subtrees. This work explores a new reranking approach for dependency parsing that can utilize complex subtree representations by applying efficient subtree selection methods. We demonstrate the effectiveness of the approach in experiments conducted on the Penn Treebank and the Chinese Treebank. Our system achieves the best performance among known supervised systems evaluated on these datasets, improving the baseline accuracy from 91.88% to 93.42% for English, and from 87.39% to 89.25% for Chinese.

本文言語English
論文番号2327295
ページ(範囲)1208-1218
ページ数11
ジャーナルIEEE Transactions on Audio, Speech and Language Processing
22
7
DOI
出版ステータスPublished - 2014 7 1
外部発表はい

ASJC Scopus subject areas

  • 音響学および超音波学
  • 電子工学および電気工学

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