计算机科学 ›› 2023, Vol. 50 ›› Issue (3): 291-297.doi: 10.11896/jsjkx.220700146
环志刚1,2, 蒋国权2, 张玉健1, 刘浏2,3, 刘姗姗2
HUAN Zhigang1,2, JIANG Guoquan2, ZHANG Yujian1, LIU Liu2,3, LIU Shanshan2
摘要: 事件共指消解是很多自然语言处理任务的基础,旨在识别文本中指代相同真实事件的事件提及。由于中文语法相比英文更复杂,捕获英文文本特征的方法在中文事件共指消解中效果并不明显。为解决文档内中文事件共指,提出了一种门控机制神经网络(Gated Mechanism Neural Network,GMNN)。针对中文具有主语省略、结构松散等特点,引入事件基本属性作为符号特征。在此基础上,提出了一种新的门控去噪机制,对符号特征向量进行微调,过滤符号特征中的噪声,提取在特定上下文语境中的有用信息,进而提高共指事件的识别率。在ACE2005中文数据集上进行了实验,结果表明,GMNN的AVG分数提升了2.66,有效地提高了中文事件共指消解的效果。
中图分类号:
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