Semantic Pivoting Model for Effective Event Detection
This work improves event detection for NLP applications by introducing a more efficient method, though it appears incremental as it builds on existing techniques by adding semantic information.
The paper tackles event detection in NLP by addressing the inefficiency of using one-hot vectors for event types, proposing a Semantic Pivoting Model (SPEED) that incorporates semantic meaning, and reports state-of-the-art performance in multiple settings.
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.