Overlap Event Extraction Method with Language Granularity Fusion Based on Joint Learning
Event extraction is a crucial task in information extraction.The existing event extraction methods generally assume that only one event occurs in a sentence.However,overlapping events are inevitable in real scenarios.Therefore,this paper de-signs an overlap event extraction method with language granularity fusion based on joint learning.In this method,a strategy of in-creasing and decreasing token number layer by layer is designed to represent fragments of different language granularity.On this basis,a sentence representation of progressive language granularity fusion is constructed.By introducing event information per-ception,the sentence representation of language granularity and event information fusion based on gating mechanism is estab-lished.Finally,through the joint study of the fragment relationship and role relationship between words,the identification of event triggering words,arguments,event types and argument roles is realized.The experiments conducted on the FewFC and DuEE1.0-1 datasets demonstrate that the LGFEE model proposed in this paper achieves an improvement of 0.8％and 0.6％in the F1 score for event type discrimination tasks,respectively.Furthermore,it also exhibits higher recall rates and F1 scores in trigger word recognition,argument recognition,and argument role classification tasks,which verifies the validity of LGFEE model.
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NSTL
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