计算机科学 ›› 2020, Vol. 47 ›› Issue (3): 231-236.doi: 10.11896/jsjkx.190100108
付健,孔芳
FU Jian,KONG Fang
摘要： 随着深度学习的兴起与发展,越来越多的学者开始将深度学习技术应用于指代消解任务中。但现有的神经指代消解模型普遍只关注文本的线性特征,忽略了传统方法中已证明非常有效的结构信息的融入。以目前表现最佳的Lee等提出的神经网络模型为基础,借助成分句法树对上述问题进行了改进:1)提出了一种枚举句法树中以结点为短语的抽取策略,避免了暴力枚举策略所受到的长度限制与不符合句法规则的短语集噪音的引入;2)利用树的遍历得到结点序列,结合结点的高度与路径等特征,直接对成分句法树进行上下文表示并将其融入模型中,避免了只使用字、词序列而产生的结构信息缺失问题。在CoNLL 2012 Shared Task的数据集上对所提模型进行了一系列实验,实验结果显示,其中文指代消解的F1值达到了62.35,英文指代消解的F1值也达到了67.24,从而验证了所提结构信息融入策略能大大提升指代消解的性能。
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