TAED: Topic-Aware Event Detection

Abstract

Identifying event trigger words and classifying event types known as the event detection task is a fundamental step for extracting event-related knowledge from textual sources. Examples of the topics within documents include "military conflict," "earthquake," "concert tour," "wrestling," and others. Topical information embedded within documents where the events are extracted from is rarely explored. Rich topic information could be a helpful indicator of the event's type. Semantically similar topics share similar event types, while event types are quite different between distinguishable document topics. In this paper, we explored a novel method of integrating document topic information to complete the event detection task. We summarized our contribution as the following: we used the topic information of the documents to generate topic comprehensive sentence representations. We adopted a multi-task deep neural network, trained with event detection and topic classification t asks. We evaluated our method with two datasets that are designed for more diverse and general event types event detection MAVEN [1] and RAMS [2]. We demonstrated that the topic-aware model outperformed the baseline model F 1 score on both MAVEN and RAMS datasets. An analysis in the few-shot event types scenario showed that topic-aware model can beat the baseline by up to 13.34% on the F 1 score for the rare event types.