关键词: 论元抽取,马尔科夫逻辑网络,论元推理

Abstract: Currently,previous Chinese argument extraction approaches mainly use syntactic structure as the major features to describe the relationship between trigger and its arguments.However,they suffer much from those inter-sentence arguments which are not in the same sentence or clause of the trigger.To address this issue,this paper brought forward a novel argument inference mechanism based on the Markov logic network.It first learns the probabilities of an entity fulfilling a specific role from the training set and obtains those extracted argument mentions with high confidences in the test set.Then it uses them to extract those argument mentions with lack of effective context information or low confidences.Experimental results on the ACE 2005 Chinese corpus show that our approach outperforms the baseline significantly,with the improvements of 6.0% and 4.4% in argument identification and role determination respectively.

Key words: Argument extraction,Markov logic network,Argument inference