Abstract
In this paper, we propose a novel feature enrichment method for short text classification based on the link analysis on topic-keyword graph. After topic modeling, we re-rank the keywords distribution extracted by biterm topic model (BTM) to make the topics more salient. Then a topic-keyword graph is constructed and link analysis is conducted. For complement, the K-L divergence is integrated with the structural similarity to discover the most related keywords. At last, the short text is expanded by appending these related keywords for classification. Experimental results on two open datasets validate the effectiveness of the proposed method.
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Wang, P., Zhang, H., Xu, B., Liu, C., Hao, H. (2014). Short Text Feature Enrichment Using Link Analysis on Topic-Keyword Graph. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_8
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DOI: https://doi.org/10.1007/978-3-662-45924-9_8
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