Semantic relation extraction based on semi-supervised learning

Haibo Li*, Yutaka Matsuo, Mitsuru Ishizuka

*この研究の対応する著者

研究成果: Conference contribution

抄録

Many tasks of information extraction or natural language processing have a property that the data naturally consist of several views-disjoint subsets of features. Specifically, a semantic relationship can be represented with some entity pairs or contexts surrounding the entity pairs. For example, the Person-Birthplace relation can be recognized from the entity pair view, such as (Albert Einstein, Ulm), (Pablo Picasso, Malaga) and so on. On the other side, this relation can be identified with some contexts, such as "A was born in B", "B, the birth place of A" and so on. To leverage the unlabeled data in the training stage, semi-supervised learning has been applied to relation extraction task. In this paper, we propose a multi-view semi-supervised learning algorithm, Co-Label Propagation, to combine the 'information' from both the entity pair view and the context view. In propagation process, the label scores of classes are spread not only in the entity pair view and the context view, but also between the two views. The proposed algorithm is evaluated using semantic relation classification tasks. The experiment results validate its effectiveness.

本文言語English
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ページ270-279
ページ数10
6458 LNCS
DOI
出版ステータスPublished - 2010
外部発表はい
イベント6th Asia Information Retrieval Societies Conference, AIRS 2010 - Taipei
継続期間: 2010 12 12010 12 3

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6458 LNCS
ISSN(印刷版)03029743
ISSN(電子版)16113349

Other

Other6th Asia Information Retrieval Societies Conference, AIRS 2010
CityTaipei
Period10/12/110/12/3

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 理論的コンピュータサイエンス

フィンガープリント

「Semantic relation extraction based on semi-supervised learning」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