Negation Scope Detection with Recurrent Neural Networks Models in Review Texts  

作　　者：Lydia Lazib Yanyan Zhao Bing Qin Ting Liu 

机构地区：[1]Research Center for Social Computing and Information Retrieval,Harbin Institute of Technology,Harbin150001,China

出　　处：《国际计算机前沿大会会议论文集》2016年第1期127-130,共4页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

摘　　要：Identifying negation scopes in a text is an important subtask of information extraction, that can benefit other natural language processing tasks, like relation extraction, question answering and sentiment analysis. And serves the task of social media text understanding. The task of negation scope detection can be regarded as a token-level sequence labeling problem. In this paper, we propose different models based on recurrent neural networks (RNNs) and word embedding that can be successfully applied to such tasks without any task-specific feature engineering efforts. Our experimental results show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based model.

关 键 词：NEGATION SCOPE DETECTION Natural LANGUAGE processing RECURRENT neural networks 

分 类 号：C5[社会学]