Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (7): 287-295.doi: 10.11896/jsjkx.230700118

• Artificial Intelligence • Previous Articles     Next Articles

Overlap Event Extraction Method with Language Granularity Fusion Based on Joint Learning

YAN Jingtao1, LI Yang2, WANG Suge1,3, PAN Bangze1   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 School of Finance,Shanxi University of Finance and Economics,Taiyuan 030006,China
    3 Key Laboratory Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan 030006,China
  • Received:2023-07-17 Revised:2023-12-09 Online:2024-07-15 Published:2024-07-10
  • About author:YAN Jingtao,born in 1999,postgra-duate.Her main research interest is natural language processing.
    LI Yang,born in 1988,Ph.D,associate professor,is a member of CCF(No.33621G).Her main research interests include text sentiment analysis and text mining.
  • Supported by:
    National Key Research and Development Program of China(2022QY0300-01),National Natural Science Foundation of China(62106130) and Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(2021L284).

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Abstract: 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 designs an overlap event extraction method with language granularity fusion based on joint learning.In this method,a strategy of increasing 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 perception,the sentence representation of language granularity and event information fusion based on gating mechanism is established.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 DuEE1.0-1datasets 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.

Key words: Overlapping event extraction, Language granularity fusion, Joint learning, Attention mechanism, Gating mechanism

CLC Number: 

  • TP391
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