FGCAN: Filter-based Gated Contextual Attention Network for event detection

Abstract

Contextual information is widely used in natural language processing and is important for event detection. How to make full use of contextual information is a challenging problem. Traditional event detection methods mainly use sentence-level information to identify event triggers and classify them into specific types. Event trigger is defined as the word or phrase that most clearly expresses an event occurrence. However, the information used for detecting events is usually spread across multiple sentences, and sentence-level information is often insufficient to resolve ambiguities for some types of events. In this paper, we propose a novel Filter-based Gated Contextual Attention Network model called FGCAN, which is augmented with hierarchical contextualized representations to utilize both sentence-level and document-level information. In document level, we construct a gated contextual attention layer to extract document-level information by considering the relatedness between the current and other sentences and dynamically incorporate it into words. In this way, we can get cross-sentence clues without designing complex inference rules. In sentence level, we feed sentences into the classifier to get global information of them, and devise a rule-based filter algorithm to rectify the prediction of each word based on the probability ranking of the sentence labels, which is highly interpretable. These two mechanisms focusing on different scopes of contextual information can complement each other. The experimental results on the widely used ACE 2005 and KBP 2015 datasets show that our approach outperforms the state-of-the-art methods and the two components are effective in using contextual information.