Topic Segmentation of Web Documents with Automatic Cue Phrase Identification and BLSTM-CNN

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.

关键词： topic segmentation neural network web documents sequence mining

作者: Liang Wang Sujian Li Xinyan Xiao Yajuan Lyu

作者单位: Key Laboratory of Computational Linguistics,Peking University,MOE,China Key Laboratory of Computational Linguistics,Peking University,MOE,China;Collaborative Innovation Cen Baidu Inc.,Beijing,China

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-12

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）