首页|Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network
Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network
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NETL
NSTL
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Event detection(ED)seeks to recognize event triggers and classify them into the predefined event types.Chinese ED is formulated as a character-level task owing to the uncertain word boundaries.Prior methods try to incorpo-rate word-level information into characters to enhance their semantics.However,they experience two problems.First,they fail to incorporate word-level information into each character the word encompasses,causing the insufficient word-charac-ter interaction problem.Second,they struggle to distinguish events of similar types with limited annotated instances,which is called the event confusing problem.This paper proposes a novel model named Label-Aware Heterogeneous Graph Attention Network(L-HGAT)to address these two problems.Specifically,we first build a heterogeneous graph of two node types and three edge types to maximally preserve word-character interactions,and then deploy a heterogeneous graph attention network to enhance the semantic propagation between characters and words.Furthermore,we design a pushing-away game to enlarge the predicting gap between the ground-truth event type and its confusing counterpart for each character.Experimental results show that our L-HGAT model consistently achieves superior performance over prior competitive methods.
Chinese event detectionheterogeneous graph attention network(HGAT)label embedding
崔诗尧、郁博文、从鑫、柳厅文、谭庆丰、时金桥
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Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100190,China
School of Cyber Security,University of Chinese Academy of Sciences,Beijing 100049,China
Cyberspace Institute of Advanced Technology,Guangzhou University,Guangzhou 510006,China
School of Cyber Security,Beijing University of Posts and Telecommunications,Beijing 100088,China
NETL
NSTL
万方数据
