Abstract
To alleviate sparseness in short text clustering, considerable researches investigate external information such as Wikipedia to enrich feature representation, which requires extra works and resources and might lead to possible inconsistency. Sparseness leads to weak connections between short texts, thus the similarity information is difficult to be measured. We introduce a special term-specific document set—potential locality set—to capture weak similarity. Specifically, for any two short documents within the same potential locality, the Jaccard similarity between them is greater than 0. In other words, the adjacency graph based on these weak connections is a complete graph. Further, a locality-sensitive term weighting scheme is proposed based on our potential locality set. Experimental results show the proposed approach builds more reliable neighborhood for short text data. Compared with another state-of-the-art algorithm, the proposed approach obtains better clustering performances, which verifies its effectiveness.
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Notes
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83% text-based recommender systems in the domain of digital libraries, see https://en.wikipedia.org/wiki/Tf-idf.
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idf has a probabilistic explanation of the odds that t occurs in d.
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Acknowledgement
The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 11300715], and a grant from City University of Hong Kong [Project No. 7004674].
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Zheng, CT., Qian, S., Cao, WM., Wong, HS. (2017). Locality-Sensitive Term Weighting for Short Text Clustering. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_46
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DOI: https://doi.org/10.1007/978-3-319-70087-8_46
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