Semantic Pivoting Model for Effective Event Detection

• URL: http://arxiv.org/abs/2211.00709v1
• Date: Tue, 1 Nov 2022 19:20:34 GMT
• Title: Semantic Pivoting Model for Effective Event Detection
• Authors: Anran Hao, Siu Cheung Hui, Jian Su
• Abstract summary: Event Detection aims to identify and classify mentions of event instances from unstructured articles. Existing techniques for event detection only use homogeneous one-hot vectors to represent the event type classes, ignoring the fact that the semantic meaning of the types is important to the task. We propose a Semantic Pivoting Model for Effective Event Detection (SPEED), which explicitly incorporates prior information during training and captures semantically meaningful correlations between input and events.
• Score: 19.205550116466604
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Event Detection, which aims to identify and classify mentions of event instances from unstructured articles, is an important task in Natural Language Processing (NLP). Existing techniques for event detection only use homogeneous one-hot vectors to represent the event type classes, ignoring the fact that the semantic meaning of the types is important to the task. Such an approach is inefficient and prone to overfitting. In this paper, we propose a Semantic Pivoting Model for Effective Event Detection (SPEED), which explicitly incorporates prior information during training and captures semantically meaningful correlations between input and events. Experimental results show that our proposed model achieves state-of-the-art performance and outperforms the baselines in multiple settings without using any external resources.

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