Abstract
Due to the limited length and freely constructed sentence structures, short text is different from normal text, which makes traditional algorithm of text representation does not work well on it. This paper proposes a model called Conceptual and Semantic Enrichment with Topic Model (CSET) by combining Biterm Topic Model (BTM), a widely used probabilistic topic model which is designed for short text with Probase, a large-scale probabilistic knowledge base. CSET is able to capture semantic relations between words to enrich short text. Our model enables large amount of applications that rely on semantic understanding of short text, including short text classification and word similarity measurement in context.
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Acknowledgement
This work is supported in part by the National Natural Science Foundation of China under Grant 61170035, 61272420 and 81674099, Six talent peaks project in Jiangsu Province (Grant No. 2014 WLW-004), the Fundamental Research Funds for the Central Universities (Grant No. 30916011328, 30918015103), Jiangsu Province special funds for transformation of science and technology achievement (Grant No. BA2013047).
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Shi, Q., Wang, Y., Sun, J., Fu, A. (2018). Short Text Understanding Based on Conceptual and Semantic Enrichment. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_28
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DOI: https://doi.org/10.1007/978-3-030-05090-0_28
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