计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 244-249.doi: 10.11896/jsjkx.190900056
周新宇, 李培峰
ZHOU Xin-yu, LI Pei-feng
摘要: 事件时序关系分类任务是信息抽取领域的一个分支,由于其对多个自然语言处理任务具有很好的辅助作用,近年来得到了越来越多的关注。目前,已有的神经网络方法对事件间信息交互的考虑相对缺乏。针对这个问题,提出了一种通过参数共享来增强事件间信息交互的方法。该方法首先通过门控卷积神经网络(Gated Convolutional Neural Network,GCNN)学习句子的语义信息和上下文信息,并将其融入最短依存路径序列作为输入;然后使用双向长短期记忆网络(Bidirectional Long Short-Term Memory network,Bi-LSTM)对输入进行编码以获取其语义表示,并通过参数共享来增强事件之间的信息交互;最后将获得的语义表示输入全连接层,使用Softmax函数进行分类预测。TimeBank-Dense语料库上的实验结果表明,所提方法在分类精度上优于现有的大多数神经网络方法。
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