Graph Convolutional Networks With Syntactic and Semantic Structures for Event Detection

Abstract

Event detection is an important task for information extraction, which seeks to identify instances of specific event types from pieces of text. Recent studies have suggested that incorporating syntactic dependency graphs as feature representations for graph neural networks can significantly boost event detection performance. However, there are still challenges in leveraging multi-hop relationships within dependency parse trees to provide valuable additional information for keywords, as well as in effectively extracting relevant information from subordinate clauses, such as restrictive clauses. In this paper, we propose a novel Graph Convolutional Networks With Syntactic and Semantic (GCNWSS) structures for event detection task. Specifically, we construct a multi-hop matrix as the syntactic structure that calculates the hop distance between each word-pair. Besides, we propose a combination of biaffine attention and trigger-aware attention to generate semantic structures. In which, The biaffine attention mechanism is used to capture the global semantic information in a sentence. The trigger-aware attention mechanism enables the learning of trigger-related local semantics features of the text. Experimental results on benchmark dataset illustrate that our proposed model outperforms state-of-the-art methods.

Keywords

• Event detection
• graph neural network
• attention mechanism
• dependency parsing tree