Feature distance-based framework for classification of low-frequency semantic relations

André Kenji Horie*, Mitsuru Ishizuka

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In the relation extraction of semantic relations, it is not uncommon to face settings in which the training data provides very few instances of some relation classes. This is mostly due to the high cost of producing such data and to the class imbalance problem, which may result in some classes presenting small frequencies even with a large annotated corpus. This work thus presents a semi-supervised bootstrapped method to expand this initial training dataset, using pattern matching to extract new candidate instances from the Web. The core of this process uses a multiview feature distance-based framework, which allows quantitative and qualitative analysis of intermediate steps of the process. Experimental results show that this framework provides better results in the relation classification task than the baseline, and the bootstrapped architecture improves the relation classification task as a whole for these low-frequency semantic relations settings.

本文言語English
ホスト出版物のタイトルProceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011
ページ59-66
ページ数8
DOI
出版ステータスPublished - 2011
外部発表はい
イベント5th Annual IEEE International Conference on Semantic Computing, ICSC 2011 - Palo Alto, CA
継続期間: 2011 9月 182011 9月 21

Other

Other5th Annual IEEE International Conference on Semantic Computing, ICSC 2011
CityPalo Alto, CA
Period11/9/1811/9/21

ASJC Scopus subject areas

  • 計算理論と計算数学
  • コンピュータ サイエンスの応用
  • 理論的コンピュータサイエンス

フィンガープリント

「Feature distance-based framework for classification of low-frequency semantic relations」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