A fast and effective method for clustering large-scale Chinese question dataset | |
Zhang, Xiaodong ; Wang, Houfeng | |
刊名 | communications in computer and information science
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2014 | |
英文摘要 | 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-the-art methods and cost less time. ? Springer-Verlag Berlin Heidelberg 2014.; EI; 0; 345-356; 496 |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.pku.edu.cn/handle/20.500.11897/327890] ![]() |
专题 | 信息科学技术学院 |
推荐引用方式 GB/T 7714 | Zhang, Xiaodong,Wang, Houfeng. A fast and effective method for clustering large-scale Chinese question dataset[J]. communications in computer and information science,2014. |
APA | Zhang, Xiaodong,&Wang, Houfeng.(2014).A fast and effective method for clustering large-scale Chinese question dataset.communications in computer and information science. |
MLA | Zhang, Xiaodong,et al."A fast and effective method for clustering large-scale Chinese question dataset".communications in computer and information science (2014). |
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