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Bigram Anchor Words Topic Model

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Analysis of Images, Social Networks and Texts (AIST 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 661))

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Abstract

A probabilistic topic model is a modern statistical tool for document collection analysis that allows extracting a number of topics in the collection and describes each document as a discrete probability distribution over topics. Classical approaches to statistical topic modeling can be quite effective in various tasks, but the generated topics may be too similar to each other or poorly interpretable. We supposed that it is possible to improve the interpretability and differentiation of topics by using linguistic information such as collocations while building the topic model. In this paper we offer an approach to accounting bigrams (two-word phrases) for the construction of Anchor Words Topic Model.

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Acknowledgments

This work was supported by grant RFFI 14-07-00383A Research of methods of integration of linguistic knowledge into statistical topic models.

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Correspondence to Natalia Loukachevitch .

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Ashuha, A., Loukachevitch, N. (2017). Bigram Anchor Words Topic Model. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-52920-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52919-6

  • Online ISBN: 978-3-319-52920-2

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