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	Topic Segmentation of Web Documents with Automatic Cue Phrase Identification and BLSTM-CNN
	Wang, Liang ; Li, Sujian ; Xiao, Xinyan ; Lyu, Yajuan
	2016
关键词	Topic segmentation Neural network Web documents Sequence mining TEXT SEGMENTATION
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 short-term 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.; Baidu-Peking University; National Natural Science Foundation of China [61273278, 61572049]; CPCI-S(ISTP); 177-188; 10102
语种	英语
出处	5th International Conference on Natural Language Processing and Chinese Computing (NLPCC) / 24th International Conference on Computer Processing of Oriental Languages (ICCPOL)
DOI标识	10.1007/978-3-319-50496-4_15
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470110]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wang, Liang,Li, Sujian,Xiao, Xinyan,et al. Topic Segmentation of Web Documents with Automatic Cue Phrase Identification and BLSTM-CNN. 2016-01-01.

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