Negation scope detection with a conditional random field model  

作　　者：Lydia Lazib Zhao Yanyan Qin Bing Liu Ting 

机构地区：[1]Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin 150001, P. R. China

出　　处：《High Technology Letters》2017年第2期191-197,共7页高技术通讯（英文版）

基　　金：Supported by the National High Technology Research and Development Programme of China(No.2015AA015407);the National Natural Science Foundation of China(No.61273321);the Specialized Research Fund for the Doctoral Program of Higher Education(No.20122302110039)

摘　　要：Identifying negation cues and their scope in a text is an important subtask of information extraction that can benefit other natural language processing tasks,including but not limited to medical data mining,relation extraction,question answering and sentiment analysis.The tasks of negation cue and negation scope detection can be treated as sequence labelling problems.In this paper,a system is presented having two components:negation cue detection and negation scope detection.In the first phase,a conditional random field(CRF) model is trained to detect the negation cues using a lexicon of negation words and some lexical and contextual features.Then,another CRF model is trained to detect the scope of each negation cue identified in the first phase,using basic lexical and contextual features.These two models are trained and tested using the dataset distributed within the* Sem Shared Task 2012 on resolving the scope and focus of negation.Experimental results show that the system outperformed all the systems submitted to this shared task.Identifying negation cues and their scope in a text is an important subtask of information extraction that can benefit other natural language processing tasks,including but not limited to medical data mining,relation extraction,question answering and sentiment analysis.The tasks of negation cue and negation scope detection can be treated as sequence labelling problems.In this paper,a system is presented having two components：negation cue detection and negation scope detection.In the first phase,a conditional random field（CRF） model is trained to detect the negation cues using a lexicon of negation words and some lexical and contextual features.Then,another CRF model is trained to detect the scope of each negation cue identified in the first phase,using basic lexical and contextual features.These two models are trained and tested using the dataset distributed within the＊ Sem Shared Task 2012 on resolving the scope and focus of negation.Experimental results show that the system outperformed all the systems submitted to this shared task.

关 键 词：negation detection negation cue detection negation scope detection natural language processing 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]