Semantic relation extraction based on semi-supervised learning

Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages270-279
Number of pages10
Volume6458 LNCS
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event6th Asia Information Retrieval Societies Conference, AIRS 2010 - Taipei
Duration: 2010 Dec 12010 Dec 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6458 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

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

Keywords

  • multi-view learning
  • relation extraction
  • semi-supervised learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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    Li, H., Matsuo, Y., & Ishizuka, M. (2010). Semantic relation extraction based on semi-supervised learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6458 LNCS, pp. 270-279). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6458 LNCS). https://doi.org/10.1007/978-3-642-17187-1_26