基于BLSTM网络的医学时间短语识别

Temporal phrases extraction in clinical text based on bidirectional long-short term memory model

从医学文本中识别时间短语是临床医学自然语言处理的关键技术之一。传统基于规则和机器学习的方法需要设计复杂规则和提取特征,而且大多数系统采用串行方法会导致错误的传播。提出了一种基于双向长短时记忆网络(BLSTM)的神经网络架构,在识别时间表示式的同时判别它们的类型:首先使用卷积神经网络(CNN)学习得到单词的字符级别向量和大规模生物医学背景语料上训练得到的词向量进行组合作为BLSTM的输入,然后使用BLSTM网络学习单词的上下文语义表示,最后使用条件随机场(CRF)对BLSTM输出的序列进行标签优化。实验基于SemEval-2016 task 12,结果表明没有添加任何特征的神经网络学习方法比该任务中官方提供的最高分的F1值提高了3%。

Recognizing time phrases from clinical text is a fundamental task for many applications in clinical NLP.Traditional methods based on rules and machine learning require the design of complex rules and feature extraction,and the serial method used by most systems may lead to error propagation.This paper proposed a novel neural network based on bidirectional long-short term memory(BLSTM)to identifying clinical time expressions and the type of them simultaneously.Firstly,it combined character-level word embedding trained by convolutional neural network(CNN)with word embedding trained from large-scale biomedical corpus together as input to BLSTM.Then it utilized BLSTM to model context information of each word.Finally,it employed conditional random field(CRF)to optimize the output of BLSTM.This paper evaluated the model task 12 of on the Semeval-2016.It receives the best F1 value without requiring any handcrafted features or rules.Compared with the state-of-the-art systems in this task,the proposed model improves the F1 scores by 3%.