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- W4319309568 abstract "Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Existing graph neural network-based models have achieved promising progress to alleviate this problem by capturing different orders of syntactic information, but they are limited by two issues. First, the long-range syntactic information between words is not fully exploited. Second, they ignore the semantic information provided by dependency labels which provide linguistic knowledge that is useful to ED. As a result, we proposed a label-enhanced dense graph convolutional network that employs dense connectivity and Graph Transformer Networks (GTN) to learn a flexible selection of edge types and composite relations between the words. Each layer can make use of the collective knowledge of dense blocks in order to model syntactic dependencies over long distances through dense connectivity. The proposed model achieves state-of-the-art performance for ED on common datasets after extensive experiments are conducted to show its advantages." @default.
- W4319309568 created "2023-02-08" @default.
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- W4319309568 date "2022-11-01" @default.
- W4319309568 modified "2023-09-26" @default.
- W4319309568 title "Label Enhanced Event Detection with Collective Knowledge and Heterogeneous Graph" @default.
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- W4319309568 doi "https://doi.org/10.1109/ickg55886.2022.00046" @default.
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