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Maximum Lexical Cohesion for Fine-Grained News Story Segmentation
文献类型:会议
作者:Liu, Zihan[1]  Xie, Lei[2]  Feng, Wei[3]  
机构:NW Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
年:2010
通讯作者:Liu, ZH (reprint author), NW Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
会议名称:11TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2010 (INTERSPEECH 2010), VOLS 1-2
页码范围:1301-1304
收录情况:EI(20112714117375)  CPCI-S(WOS:000294382400322)  
所属部门:计算机学院
人气指数:1262
浏览次数:1251
被引频次:2
语言:外文
关键词:story segmentation; KL-divergence; lexical cohesion; word weighting; dynamic programming; spoken document segmentation; spoken document retrieval
摘要:
We propose a maximum lexical cohesion (MLC) approach to news story segmentation. Unlike sentence-dependent lexical methods, our approach is able to detect story boundaries at finer word/subword granularity, and thus is more suitable for speech recognition transcripts which have no sentence delimiters. The proposed segmentation goodness measure takes account of both lexical cohesion and a prior preference of story length. We measure the lexical cohesion of a segment by the KL-divergence from its ...More
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