摘要:不确定和否定信息抽取，是自然语言处理领域中的重要任务和研究热点.针对不确定和否定作用范围识别任务，提出一种基于两层双向LSTM神经网络的作用范围识别方法.首先，对于从线索词到达词语的句法路径，使用第1层双向LSTM神经网络从中学习到有用特征；接着，将词法特征与句法路径特征一起组成当前词语的特征表示；最后，将作用范围识别问题看作序列标注任务，利用第2层双向LSTM神经网络界定当前线索词的作用范围.实验结果表明，所提出的模型优于其他神经网络模型，并在BioScope生物医学语料上取得了良好性能.其中，在Abstracts子语料上的不确定和否定作用范围识别精确率分别达到86.20%和80.28%.



Abstract:Speculation and negation information extraction is an important task and research focus in natural language processing (NLP). This paper proposes a two-layer bidirectional long short-term memory (LSTM) neural network model for speculation and negation scope detection. Firstly, a bidirectional LSTM neural network is utilized in the first layer to learn useful feature representations from the syntactic path which is from the cue to the token. Then, lexical features and syntactic path features are concatenated into the feature representations of the token. Finally, taking the scope detection problem as a sequence labeling task, another bidirectional LSTM neural network is employed in the second layer to identify the scope of the current cue. The experimental results show that the presented model is superior to other neural network models and attains excellent performances on BioScope corpus. Particularly, the model achieves the accuracy (percentage of correct scopes) of 86.20% and 80.28% on speculation and negation scope detection on Abstracts subcorpus, respectively.



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