Towards semi-supervised classification of discourse relations using feature correlations

Hugo Hernault, Danushka Bollegala, Mitsuru Ishizuka

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Two of the main corpora available for training discourse relation classifiers are the RST Discourse Treebank (RST-DT) and the Penn Discourse Treebank (PDTB), which are both based on the Wall Street Journal corpus. Most recent work using discourse relation classifiers have employed fully-supervised methods on these corpora. However, certain discourse relations have little labeled data, causing low classification performance for their associated classes. In this paper, we attempt to tackle this problem by employing a semi-supervised method for discourse relation classification. The proposed method is based on the analysis of feature cooccurrences in unlabeled data. This information is then used as a basis to extend the feature vectors during training. The proposed method is evaluated on both RST-DT and PDTB, where it significantly outperformed baseline classifiers. We believe that the proposed method is a first step towards improving classification performance, particularly for discourse relations lacking annotated data.

本文言語English
ホスト出版物のタイトルProceedings of the SIGDIAL 2010 Conference: 11th Annual Meeting of the Special Interest Group onDiscourse and Dialogue
ページ55-58
ページ数4
出版ステータスPublished - 2010
外部発表はい
イベント11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2010 - Tokyo
継続期間: 2010 9 242010 9 25

Other

Other11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2010
CityTokyo
Period10/9/2410/9/25

ASJC Scopus subject areas

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用
  • モデリングとシミュレーション

フィンガープリント

「Towards semi-supervised classification of discourse relations using feature correlations」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル