A Trigger-Aware Multi-task Learning for Chinese Event Entity Recognition

Abstract

This paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task, it only identifies the named entities which are related to a specific event type. Currently, there is no specific model to directly deal with the EER task. Previous named entity recognition methods that combine both relation extraction and argument role classification (named NER+TD+ARC) can be adapted for the task, by utilizing the relation extraction component for event trigger detection (TD). However, these technical alternatives heavily rely on the efficacy of the event trigger detection, which have to require the tedious yet expensive human labeling of the event triggers, especially for languages where triggers contain multiple tokens and have numerous synonymous expressions (such as Chinese). In this paper, a novel trigger-aware multi-task learning framework (TAM), which jointly performs both trigger detection and event entity recognition, is proposed to tackle Chinese EER task. We conduct extensive experiments on a real-world Chinese EER dataset. Compared with the previous methods, TAM outperforms the existing technical alternatives in terms of F1 measure. Besides, TAM can accurately identify the synonymous expressions that are not included in the trigger dictionary. Moreover, TAM can obtain a robust performance when only a few labeled triggers are available.