Leveraging Hierarchical Deep Semantics to Classify Implicit Discourse Relations via Mutual Learning Method

摘要：

　　This paper presents a mutual learning method using hierarchical deep semantics for the classification of implicit discourse relations in English.With the absence of explicit discourse markers,traditional discourse techniques mainly concentrate on discrete linguistic features in this task,which always leads to data sparse problem.To relieve this problem,we propose a mutual learning neural model which makes use of multilevel semantic information together,including the distribution of implicit discourse relations,the semantics of arguments and the co-occurrence of words.During the training process,the predicted target of the model which is the probability of the discourse relation type,and the distributed representation of semantic components are learnt jointly and optimized mutually.The results of both binary and multiclass identification show that this method outperforms previous works since the mutual learning strategy can distinguish Expansion type from the others efficiently.

关键词： Implicit discourse relation classification Hierarchical deep semantics Mutual learning neural network

作者: Xiaohan She Ping Jian Pengcheng Zhang Heyan Huang

作者单位: School of Computer Science and Technology,Beijing Institute of Technology School of Computer Science and Technology,Beijing Institute of Technology;Beijing Engineering Resear

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: 1-11

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）