Joint Event Extraction Based on Global Event-Type Guidance and Attention Enhancement

Abstract

Event extraction is one of the most challenging tasks in information extraction. It is a common phenomenon where multiple events exist in the same sentence. However, extracting multiple events is more difficult than extracting a single event. Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long. In addition, the current argument extraction relies on the results of syntactic dependency analysis, which is complicated and prone to error transmission. In order to solve the above problems, a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this work. Specifically, for multiple event detection, we propose a globaltype guidance method that can detect event types in the candidate sequence in advance to enhance the correlation information between events. For argument extraction, we converted it into a table-filling problem, and proposed a tablefilling method of the attention mechanism, that is simple and can enhance the correlation between trigger words and arguments. The experimental results based on the ACE 2005 dataset showed that the proposed method achieved 1.6% improvement in the task of event detection, and obtained state-of-the-art results in the argument extraction task, which proved the effectiveness of the method.

Combing Type-Aware Attention and Graph Convolutional Networks for燛vent燚etection

“…Deep neural networks have been proven to be effective in several domains, such as natural language processing, computer vision, and audio analysis [18]. However, the existing successes have mainly established on data with an underlying grid-like structure or Euclidean space.…”