模式识别与人工智能
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  2014, Vol. 27 Issue (7): 623-630    DOI:
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Ranking Topic Models without Query
XIAO Zhi-Bo, CHE Feng, WU Di, LI Qing-Feng, LU Ming-Yu
Information Science and Technology College, Dalian Maritime University, Dalian 116026

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Abstract  Topic models have become important tools in machine learning and natural language processing, which can discover hidden topics in large-scale corpus. However, as the size of the corpus grows, the scale of discovered topics grows. Most topic models are on the basis of bag-of-words model, and the orders between terms cannot be described, which makes topics undistinguishable from each other. Ranking topic models without query framework is proposed in this paper, in which topics are ranked to get ordered topic list according to their relationships. Topic relationships are used to evaluate topic influence in topic level, and term significance is used to evaluate term importance in term level and popular ranking topics with little semantics are weakened. Since there is no acknowledged evaluation criterion in ranking topic model, ranked topics are used as features to perform automatic summarization of multi-document, and the performance of ranking topic models are indirectly measured by summarization performance. The experimental results show that ranking topic models outperform topic models without ranking.
Key wordsRanking Topic Models      Evaluation of Topic Models      Multi-document Summarization      Extractive Summarization      Sentence Ranking     
Received: 26 May 2013     
ZTFLH: TP391.2  
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XIAO Zhi-Bo
CHE Feng
WU Di
LI Qing-Feng
LU Ming-Yu
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XIAO Zhi-Bo,CHE Feng,WU Di等. Ranking Topic Models without Query[J]. , 2014, 27(7): 623-630.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2014/V27/I7/623
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