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	Topic Segmentation of Web Documents with Automatic Cue Phrase Identification and BLSTM-CNN
	Liang Wang ; Sujian Li ; Xinyan Xiao ; Yajuan Lyu
	2016
关键词	topic segmentation neural network web documents sequence mining
英文摘要	Topic segmentation plays an important role for discourse analysis and document understanding.Previous work mainly focus on unsupervised method for topic segmentation.In this paper,we propose to use bidirectional long shortterm memory(BLSTM)model,along with convolutional neural network(CNN)for learning paragraph representation.Besides,we present a novel algorithm based on frequent subsequence mining to automatically discover high-quality cue phrases from documents.Experiments show that our proposed model is able to achieve much better performance than strong baselines,and our mined cue phrases are reasonable and effective.Also,this is the first work that investigates the task of topic segmentation for web documents.; 1-12
语种	英语
出处	第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)论文集中国计算机学会
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/480608]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Liang Wang,Sujian Li,Xinyan Xiao,et al. Topic Segmentation of Web Documents with Automatic Cue Phrase Identification and BLSTM-CNN. 2016-01-01.

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