门控机制融合多种特征的中文事件共指消解

Employing Gated Mechanism to Incorporate Multi-features into Chinese Event Coreference Resolution

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。

Event coreference resolution is the basis of many natural language processing tasks, aiming to identify event mentions in text that refer to the same real event.Since Chinese grammar is much more complex than English, the method of capturing English text features is not effective in Chinese event corefe-rence resolution.To solve the within-document Chinese event corefe-rence, a gated mechanism neural network(GMNN) is proposed.In view of Chinese characteristics with subject omission and loose structure, event attributes are introduced as symbolic features.On this basis, a novel gated mechanism is proposed, which fine-tunes the symbolic feature vector, filters the noise in the symbolic features, extracts useful information in a specific context, and improves the coreference events recognition rate.Experimental results on the ACE2005 Chinese dataset show that the perfor-mance of GMNN improves by 2.66,which effectively improves the effect of Chinese event coreference resolution.