A syntactic path-based hybrid neural network for negation scope detection  

作　　者：Lydia LAZIB Bing QIN Yanyan ZHAO Weinan ZHANG Ting LIU 

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

出　　处：《Frontiers of Computer Science》2020年第1期84-94,共11页中国计算机科学前沿（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(Grant Nos.61632011,61772153,71490722);Hei-longjiang philosophy and social science research project(16TQD03)。

摘　　要：The automatic detection of negation is a crucial task in a wide-range of natural language processing(NLP)applications,including medical data mining,relation extraction,question answering,and sentiment analysis.In this paper,we present a syntactic path-based hybrid neural network architecture,a novel approach to identify the scope of negation in a sentence.Our hybrid architecture has the particularity to capture salient information to determine whether a token is in the scope or not,without relying on any human intervention.This approach combines a bidirectional long shortterm memory(Bi-LSTM)network and a convolutional neural network(CNN).The CNN model captures relevant syntactic features between the token and the cue within the shortest syntactic path in both constituency and dependency parse trees.The Bi-LSTM learns the context representation along the sentence in both forward and backward directions.We evaluate our model on the Bioscope corpus,and get 90.82%F-score(78.31%PCS)on the abstract sub-corpus,outperforming features-dependent approaches.

关 键 词：natural language processing NEGATION SCOPE DETECTION convolutional NEURAL NETWORK recurrent NEURAL NETWORK SYNTACTIC path 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]