Abstract
Online topic detection (OTD) system seeks to analyze sequential stories in a real-time manner so as to detect new topics or to associate stories with certain existing topics. To handle new stories more precisely, an adaptive topic modeling method that incorporates probabilistic pseudo feedback is proposed in this paper to tune every topic model with a changed environment. Differently, this method considers every incoming story as pseudo feedback with certain probability, which is the similarity between the story and the topic. Experiment results show that probabilistic pseudo feedback brings promising improvement to online topic detection.
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Tang, G., Xia, Y. (2010). Adaptive Topic Modeling with Probabilistic Pseudo Feedback in Online Topic Detection. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds) Natural Language Processing and Information Systems. NLDB 2010. Lecture Notes in Computer Science, vol 6177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13881-2_10
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DOI: https://doi.org/10.1007/978-3-642-13881-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13880-5
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