Exploiting the Entity Type Sequence to Benefit Event Detection

Abstract

Event Detection (ED) is one of the most important tasks in the field of information extraction. The goal of ED is to find triggers in sentences and classify them into different event types. In previous works, the information of entity types are commonly utilized to benefit event detection. However, the sequential features of entity types have not been well utilized yet in the existing ED methods. In this paper, we propose a novel ED approach which learns sequential features from word sequences and entity type sequences separately, and combines these two types of sequential features with the help of a trigger-entity interaction learning module. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods.

Entity-Based Integration Framework on Social Unrest Event Detection in Social Media

“…As a crucial step in the event extraction task, event detection is one of the essential tasks in information extraction [9]. Usually, unrest events published on the discussion forum have some specific characteristics that can describe the event.…”

Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention

“…Unfortunately, there has long been a "trigger curse" which troubles the learning of event detec- tion models, especially in few-shot scenario (Bronstein et al, 2015;Liu et al, 2017;Chen et al, 2018;Liu et al, 2019;Ji et al, 2019). For many event types, their triggers are dominated by several popular words, e.g., the Attack event type is dominated by war, attack, fight, fire, bomb in ACE05.…”

“…Unfortunately, due to the decisive role of triggers, directly wiping out the trigger information commonly hurts the performance (Lu et al, 2019;Liu et al, 2020b). Some previous approaches try to tackle this problem by introducing more di-versified context information like event argument information (Liu et al, 2017(Liu et al, , 2019Ji et al, 2019) and document-level information (Ji and Grishman, 2008;Liao and Grishman, 2010;Duan et al, 2017;Chen et al, 2018). However, rich context information is commonly not available for FSED, and therefore these methods can not be directly applied.…”