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A Composite Kernel Approach for Detecting Interactive Segments in Chinese Topic Documents

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Information Retrieval Technology (AIRS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8281))

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Abstract

Discovering the interactions between persons mentioned in a set of topic documents can help readers construct the background of a topic and facilitate comprehension. In this paper, we propose a rich interactive tree structure to represent syntactic, content, and semantic information in text. We also present a composite kernel classification method that integrates the tree structure with a bigram kernel to identify text segments that mention person interactions in topic documents. Empirical evaluations demonstrate that the proposed tree structure and bigram kernel are effective and the composite kernel approach outperforms well-known relation extraction and PPI methods.

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References

  1. Chen, C.C., Chen, M.C.: TSCAN: A content anatomy approach to temporal topic summarization. IEEE Transactions on Knowledge and Data Engineering 24, 170–183 (2012)

    Article  Google Scholar 

  2. Chang, Y.-C., Chuang, P.-H., Chen, C.C., Hsu, W.-L.: FISER: An effective method for detecting interactions between topic persons. In: Hou, Y., Nie, J.-Y., Sun, L., Wang, B., Zhang, P. (eds.) AIRS 2012. LNCS, vol. 7675, pp. 275–285. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  3. Collins, M., Duffy, N.: Convolution kernels for natural language. In: Proceedings of Annual Conference on Neural Information Processing Systems, pp. 625–632 (2001)

    Google Scholar 

  4. Culotta, A., Sorensen, J.: Dependency tree kernels for relation extraction. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp. 423–429 (2004)

    Google Scholar 

  5. Feng, A., Allan, J.: Finding and linking incidents in news. In: Proceedings of the 16th ACM International Conference on Information and Knowledge Management, pp. 821–830 (2007)

    Google Scholar 

  6. Hong, G.: Relation extraction using support vector machine. In: Proceedings of the 2nd International Joint Conference on Natural Language Processing, pp. 366–377 (2005)

    Google Scholar 

  7. Joachims, T.: Text categorization with support vector machine: learning withmany relevant features. In: Proceedings of 10th European Conference on Machine Learning, pp. 137–142 (1998)

    Google Scholar 

  8. Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics on Interactive Poster and Demonstration Sessions, pp. 178–181 (2004)

    Google Scholar 

  9. Manning, C.D., Schütze, H.: Foundations of statistical natural language processing, 1st edn. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  10. Miwa, M., Thompson, P., Ananiadou, S.: Boosting automatic event extraction from the literature using domain adaptation and coreference resolution. Bioinformatics 28(13), 1759–1766 (2012)

    Article  Google Scholar 

  11. Miyao, Y., Sagae, K., Satre, R., Matsuzaki, T., Tsujii, J.: Evaluating contributions ofnatural language parsers to protein-protein interaction extraction. Bioinformatics 25(3), 394–400 (2009)

    Article  Google Scholar 

  12. Moschitti, A.: A study on convolution kernels for shallow semantic parsing. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 21–26 (2004)

    Google Scholar 

  13. Nallapati, R., Feng, A., Peng, F., Allan, J.: Event threading within news topics. In: Proceedings of the 13th ACM International Conference on Information and Knowledge Management, pp. 446–453 (2004)

    Google Scholar 

  14. Ono, T., Hishigaki, H., Tanigam, A., Takagi, T.: Automated extraction of informationon protein-protein interactions from the biological literature. Bioinformatics 17(2), 155–161 (2001)

    Article  Google Scholar 

  15. Qian, L.H., Zhou, G.D.: Tree kernel-based protein–protein interaction extraction from biomedical literature. Journal of Biomatical Informatics 45(3), 535–543 (2012)

    Article  Google Scholar 

  16. Qian, L.H., Zhou, G.D., Zhu, Q.M., Qian, P.D.: Exploiting constituent dependencies fortree kernel-based semantic relation extraction. In: Proceedings of 22nd International Conference on Computational Linguistics, pp. 697–704 (2008)

    Google Scholar 

  17. Vernon, G.M.: Human interaction: An introduction to sociology, 1st edn. Ronald Press Co., New York (1965)

    Google Scholar 

  18. Xiao, J., Su, J., Zhou, G.D., Tan, C.L.: Protein-protein interaction extraction: a supervisedlearning approach. In: Proceedings of the 1st International Symposium on Semantic Mining in Biomedicine, pp. 51–59 (2005)

    Google Scholar 

  19. Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. The Journal of Machine Learning Research 3, 1083–1106 (2003)

    MathSciNet  MATH  Google Scholar 

  20. Zhou, G.D., Qian, L.H., Fan, J.X.: Tree kernel-based semantic relation extraction with rich syntactic and semantic information. Journal of Information Science 180(8), 1313–1325 (2010)

    Article  MathSciNet  Google Scholar 

  21. Zhang, M., Zhang, J., Su, J., Zhou, G.D.: A composite kernel to extract relations between entities with both flat and structured features. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 825–832 (2006)

    Google Scholar 

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Chang, YC., Chen, C.C., Hsu, WL. (2013). A Composite Kernel Approach for Detecting Interactive Segments in Chinese Topic Documents. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_19

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  • DOI: https://doi.org/10.1007/978-3-642-45068-6_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45067-9

  • Online ISBN: 978-3-642-45068-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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