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A Model-Based EM Method for Topic Person Name Multi-polarization

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Information Retrieval Technology (AIRS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7097))

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Abstract

In this paper, we propose an unsupervised approach for multi-polarization of topic person names. We employ a model-based EM method to polarize individuals into positively correlated groups. In addition, we present off-topic block elimination and weighted correlation coefficient techniques to eliminate the off-topic blocks and reduce the text sparseness problem respectively. Our experiment results demonstrate that the proposed method can identify multi-polar person groups of topics correctly.

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Chen, C.C., Chen, ZY. (2011). A Model-Based EM Method for Topic Person Name Multi-polarization. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_37

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  • DOI: https://doi.org/10.1007/978-3-642-25631-8_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25630-1

  • Online ISBN: 978-3-642-25631-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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