Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific types of events in text. Recently, graph convolutional networks (GCNs) over dependency trees have been widely used to capture syntactic structure information and get convincing performances in event detection . However, these works ignore the syntactic relation labels on the tree, which convey rich and useful linguistic knowledge for event detection . In this paper, we investigate a novel architecture named Relation-Aware GCN (RA-GCN ), which efficiently exploits syntactic relation labels and models the relation between words specifically. We first propose a relation-aware aggregation module to produce expressive word representation by aggregating syntactically connected words through specific relation . Furthermore, a context-aware relation update module is designed to explicitly update the relation representation between words, and these two modules work in the mutual promotion way. Experimental results on the ACE2005 dataset show that our model achieves a new state-of-the-art performance for event detection .
Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks . However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED . In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN ), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types . Furthermore, to fully explore clues hidden in dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information. These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods .
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types of events
event types
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Recently,
Previous studies on the task have verified the effectiveness of integrating syntactic dependency into
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(GCNs) over dependency trees have been widely used to capture syntactic structure information and get convincing performances in event detection
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works ignore the syntactic relation labels on the tree, which convey
methods usually ignore dependency label information, which conveys
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event detection
ED
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investigate
propose
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Relation-Aware GCN (RA-GCN
Edge-Enhanced Graph Convolution Networks (EE-GCN
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efficiently exploits syntactic relation labels and models the relation between words specifically. We first propose a relation-aware aggregation module to produce expressive word representation by aggregating syntactically connected
simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected
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relation
dependency types
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a context-aware relation
to fully explore clues hidden in dependency edges, a node-aware edge
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designed to explicitly update the relation representation between words, and these two modules work in the
introduced, which refines the relation representations with contextual information. These two modules are complementary to each other and work in a
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Experimental results on the
We conduct experiments on the widely used
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show that our model achieves a new state-of-the-art performance for event detection
and the results show significant improvement over competitive baseline methods