Abstract
In this investigation, we discuss aspect tracking, i.e., how to identify tracking storylines of document topics. Since there happen huge amount of fragment information, it is hard to see what they mean and how they go within topics by hands. Here we attack to this kind of problems by means of stochastic models. Our basic idea is that we consider state transitions as internal structure of stories based on HMM, and we extract several storylines as aspects of topics by probabilistic likelihood. We utilize KL divergence to screen topics.
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- 1.
The logarithmic likelihood H(P) divided by the total size of the articles on the path, and PP = \(2^{H(P)}\).
- 2.
We examine the topic label only for evaluation purpose.
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Acknowledgment
We thank Prof. Wai Lam in Chinese University of Hong Kong for his helpful discussion and comments to our approach
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Inoue, M., Miura, T. (2018). Stochastic Approach to Aspect Tracking. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_21
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DOI: https://doi.org/10.1007/978-3-319-91947-8_21
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