Abstract
Question clustering plays an important role in QA systems. Due to data sparseness and lexical gap in questions, there is no sufficient information to guarantee good clustering results. Besides, previous works pay little attention to the complexity of algorithms, resulting in infeasibility on large-scale datasets. In this paper, we propose a novel similarity measure, which employs word relatedness as additional information to help calculating similarity between questions. Based on the similarity measure and k-means algorithm, semantic k-means algorithm and its extended version are proposed. Experimental results show that the proposed methods have comparable performance with state-of-theart methods and cost less time.
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Zhang, X., Wang, H. (2014). A Fast and Effective Method for Clustering Large-Scale Chinese Question Dataset. 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_31
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DOI: https://doi.org/10.1007/978-3-662-45924-9_31
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