基于结构化表示的中文事件同指消解方法

事件同指消解是自然语言处理中一个具有挑战性的任务,它在事件抽取、问答系统和阅读理解中具有重要作用。针对事件的语义信息主要由触发词和论元表示这一个特点,该文将事件进行结构化表示并输入一个基于门控和注意力机制的模型GAN-SR(gated attention network with structured representation),在文档内进行中文事件同指消解。首先,该模型采用语义角色标注和依存句法分析技术对事件句进行浅层语义分析,抽取事件句信息并表示为一个事件五元组。其次,将各种事件信息输入GRU进行编码,然后使用多头注意力机制挖掘事件句和事件对之间的重要特征。在ACE2005中文语料库上的实验表明,GAN-SR的性能优于目前性能最好的基准系统。

Resolving Chinese Event Coreference via Structured Representation

Event coreference resolution is a challenging task in natural language processing. Since the semantics of an event is mainly represented by its trigger and arguments, this paper introduces the structured representation of events to a neural network model GAN-SR (gated attention network with structured representation) based on the gated and attention mechanism for Chinese event coreference resolution in document level. Firstly, we apply the semantic role labeling and dependency parsing to analyze the shallow semantics of events, and then uses a quintile to represent their structures. Secondly, we use GRU to encode various kinds of event information and then apply the multi-head attention mechanism to mine the important features between the events and event pairs. The experimental results on the ACE2005 Chinese corpus show that GAN-SR outperforms the state-of-the-art baselines.