Joint Argument Inference in Chinese Event Extraction with Argument Consistency and Event Relevance
Peifeng Li, Guodong Zhou · IEEE/ACM Transactions on Audio Speech and Language Processing · 2015
Event extraction in Chinese suffers greatly from the frequent missing of arguments. Statistical analysis of the automatic content extraction (ACE) 2005 Chinese corpus shows that nearly 55% of arguments do not occur in their corresponding event mentions. This problem greatly hinders the wide deployment of event extraction in real applications. This paper proposes a novel joint argument inference model to recover those missing arguments in event mentions from a semantic perspective. In particular, this model employs various types of argument consistencies to reveal intra-event argument semantics in multiple dimensions, such as argument-argument, argument-role and argument-trigger. Moreover, this model explores several linguistic-driven event relevance phenomena, such as Coreference, Sequence, and Parallel, to unveil inter-event argument semantics in various layers, such as sentence, discourse, and document. An evaluation of the ACE 2005 Chinese corpus justifies the effectiveness of our joint argument inference model compared to a state-of-the-art baseline.