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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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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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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.

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  • 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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