计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 233-238.doi: 10.11896/jsjkx.191100031
朱培培, 王中卿, 李寿山, 王红玲
ZHU Pei-pei, WANG Zhong-qing, LI Shou-shan, WANG Hong-ling
摘要: 事件抽取是信息抽取中一个重要的研究方向其中事件检测是事件抽取的关键.目前中文神经网络事件检测方法均是基于句子的方法这种方法获得的局部上下文的信息不足以解决事件触发词的歧义性.针对这个问题文中探索了篇章信息的作用.首先以双向门控循环单元网络(Bidirectional Gated Recurrent UnitsBi-GRU)模型为基线定义3个窗口来学习句子特征;然后将句子表示进行拼接利用双向门控循环单元网络学习句子的上下文特征;最后将句子表示和上下文表示进行融合以丰富句子的语义信息并减少候选触发词语义模糊现象通过Softmax函数进行事件触发词的分类.在ACE2005数据集上的实验结果表明句子的上下文特征能够有效提升中文事件检测方法的性能该中文事件检测方法的F1值比当前最好的模型高1.5%.
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