2006 RelExtrUsingLabelPropSemiSupLearn

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Subject Headings: Relation Detection from Text Algorithm, Semi-supervised Induction Algorithm

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Abstract

Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm, a label propagation (LP) algorithm, for relation extraction. It represents labeled and unlabeled examples and their distances as the nodes and the weights of edges of a graph, and tries to obtain a labeling function to satisfy two constraints: 1) it should be fixed on the labeled nodes, 2) it should be smooth on the whole graph. Experiment results on the ACE corpus showed that this LP algorithm achieves better performance than SVM when only very few labeled examples are available, and it also performs better than bootstrapping for the relation extraction task.


References

  • Agichtein E. and Gravano L.. (2000). Snowball: Extracting Relations from large Plain-Text Collections, In: Proceedings of the 5th ACM International Conference on Digital Libraries (ACMDL’00).
  • Belkin M. and Niyogi P.. (2002). Using Manifold Structure for Partially Labeled Classification. Advances in Neural Infomation Processing Systems 15.
  • Blum A. and Chawla S. (2001). Learning from Labeled and Unlabeled Data Using Graph Mincuts. In: Proceedings of the 18th International Conference on Machine Learning.
  • Blum A., Lafferty J., Rwebangira R. and Reddy R. (2004). Semi-Supervised Learning Using Randomized Mincuts. In: Proceedings of the 21th International Conference on Machine Learning..
  • Brin Sergey. (1998). Extracting patterns and relations from world wide web. In: Proceedings of WebDB Workshop at 6th International Conference on Extending Database Technology (WebDB’98). pages 172-183.
  • Charniak E. (1999). A Maximum-entropy-inspired parser. Technical Report CS-99-12. Computer Science Department, Brown University.
  • Culotta A. and Soresen J. (2004). Dependency tree kernels for relation extraction, In: Proceedings of 42th Annual Meeting of the Association for Computational Linguistics. 21-26 July (2004). Barcelona, Spain.
  • Hasegawa T., Sekine S. and Grishman R. (2004). Discovering Relations among Named Entities from Large Corpora, In: Proceedingseeding of Conference ACL2004. Barcelona, Spain.
  • Kambhatla N. (2004). Combining lexical, syntactic and semantic features with Maximum Entropy Models for extracting relations, In: Proceedings of 42th Annual Meeting of the Association for Computational Linguistics.. 21-26 July (2004). Barcelona, Spain.
  • Lin J. (1991). Divergence Measures Based on the Shannon Entropy. IEEE Transactions on Information Theory. Vol 37, No.1, 145-150.
  • Miller S.,Fox H.,Ramshaw L. and Weischedel R. (2000). A novel use of statistical parsing to extract information from text. In: Proceedings of 6th Applied Natural Language Processing Conference 29 April-4 may 2000, Seattle USA.
  • Slonim, N., Friedman, N., and Tishby, N. (2002). Unsupervised Document Classification Using Sequential Information Maximization. In: Proceedings of the 25th ACM SIGIR Conference on Research and Development in Information Retrieval.
  • David Yarowsky. (1995). Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In: Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics. pp.189-196.
  • Zelenko D., Aone C. and Richardella A. (2002). Kernel Methods for Relation Extraction, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). Philadelphia.
  • Zhang Zhu. (2004). Weakly-supervised relation classification for Information Extraction, In: Proceedings of ACM 13th conference on Information and Knowledge Management (CIKM’2004). 8-13 Nov (2004). Washington D.C.,USA.
  • Zhou GuoDong, Su Jian, Zhang Jie and Zhang min. (2005). Exploring Various Knowledge in Relation Extraction. In: Proceedings of 43th Annual Meeting of the Association for Computational Linguistics. USA.
  • Zhu Xiaojin and Ghahramani Zoubin. (2002). Learning from Labeled and Unlabeled Data with Label Propagation. CMU CALD tech report CMU-CALD-02-107.
  • Zhu Xiaojin, Ghahramani Zoubin, and Lafferty J. (2003). Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. In: Proceedings of the 20th International Conference on Machine Learning.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2006 RelExtrUsingLabelPropSemiSupLearnChew Lim Tan
Jinxiu Chen
Donghong Ji
Zhengyu Niu
Relation Extraction Using Label Propagation Based Semi-supervised Learninghttp://wing.comp.nus.edu.sg/chime/060628/Relation extraction.pdf