A Joint Model for Causal Event Pairs Extraction without Triggers
Event Causality Extraction(ECE)extracts the event types,event arguments and causal relationships be-tween events from the text.In contrast to the previous works relying on triggers in texts,this paper investigate the task on texts without triggers and proposes a two-stage joint extraction model.In the first stage,the paper identifies the event types and causal relationships by the cascade model.In the second stage,the paper obtains sentence repre-sentations embedded with event type information by using dual localization and machine reading comprehension mechanism,and predicts the first and last positions of each event element by using a multi-layer binary tagging de-coder.To alleviate the error propagation,the two-stage model is jointly trained by shared encoding layers.It is shown that the method proposed in this paper can effectively extract causal event pairs from the target-free text in a completely rule-free situation.
万方数据
维普
