Advertisement

MFC: A Method of Co-referent Relation Acquisition from Large-Scale Chinese Corpora

  • Guogang Tian
  • Cungen Cao
  • Lei Liu
  • Haitao Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

This paper proposes a multi-feature constrained method (MFC) to acquire co-referent relations from large-scale Chinese corpora. The MFC has two phases: candidate relations extraction and verification. The extraction phase uses distribution distance, pattern homogeneity and coordination distribution features of co-referent target words to extract candidate relations from Chinese corpora. In the verification phase, we define an ontology for co-referent token words, and build a relation graph for all candidate relations. Both the ontology and the graph are integrated to generate individual, joint and reinforced strategies to verify candidate relations. Comprehensive experiments have shown that the MFC is practical, and can also be extended to acquire other types of relations.

Keywords

Target Word Recall Rate Distribution Distance Severe Acute Respiratory Syndrome Token Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hearst, M.A.: Automatic Acquisition of Hyponyms from Large Text Corpora. In: 14th International Conference on Computational Linguistics (COLING 1992), August 23-28, pp. 539–545 (1992)Google Scholar
  2. 2.
    Cederberg, S., Widdows, D.: Using LSA and Noun Coordination Information to Improve the Precision and Recall of Automatic Hyponymy Extraction. In: Conference on Natural Language Learning (CoNLL 2003), Edmonton, Canada, pp. 111–118 (2003)Google Scholar
  3. 3.
    Girju, R., Badulescu, A., Moldovan, D.: Learning Semantic Constraints for the Automatic Discovery of Part-Whole Relations. In: Proceedings of HLT-NAACL 2003, Edmonton, pp. 1–8 (May-June, 2003)Google Scholar
  4. 4.
    Berland, M., Charniak, E.: Finding Parts in Very Large Corpora. In: Proceedings of the 37th Annual Meeting of the Association for the Computational Linguistics (ACL 1999), College Park, MD, pp. 57–64 (1999)Google Scholar
  5. 5.
    Maedche, A., Staab, S.: Discovering Conceptual Relations from Text. In: Proceedings of the 14th European Conference on Artificial Intelligence (ECAI 2000), Berlin, Germany, August 20-25, pp. 321–325 (2000)Google Scholar
  6. 6.
    Lin, D., Pantel, P.: Induction of Semantic Classes from Natural Language Text. In: Proceedings of SIGKDD 2001, San Francisco, CA, USA, pp. 317–322 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guogang Tian
    • 1
    • 2
  • Cungen Cao
    • 1
  • Lei Liu
    • 1
    • 2
  • Haitao Wang
    • 1
    • 2
  1. 1.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of Sciences 
  2. 2.Graduate University of the Chinese Academy of SciencesBeijingChina

Personalised recommendations