@inproceedings{yangxiao-chenliang-2021-trigger,
title = "A Trigger-Aware Multi-Task Learning for {C}hinese Event Entity Recognition",
author = "Yangxiao, Xiang and
Chenliang, Li",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.100",
pages = "1121--1130",
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 namedentity 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 efficiency of the event trigger detection which have to require the tedious yet expensive human la-beling of the event triggers especially for languages where triggers contain multiple tokens andhave numerous synonymous expressions (such as Chinese). In this paper a novel trigger-awaremulti-task learning framework (TAM) which jointly performs both trigger detection and evententity recognition is proposed to tackle Chinese EER task. We conduct extensive experimentson a real-world Chinese EER dataset. Compared with the previous methods TAM outperformsthe existing technical alternatives in terms of F1 measure. Besides TAM can accurately identifythe synonymous expressions that are not included in the trigger dictionary. Morover TAM canobtain a robust performance when only a few labeled triggers are available.",
language = "English",
}
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<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 namedentity 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 efficiency of the event trigger detection which have to require the tedious yet expensive human la-beling of the event triggers especially for languages where triggers contain multiple tokens andhave numerous synonymous expressions (such as Chinese). In this paper a novel trigger-awaremulti-task learning framework (TAM) which jointly performs both trigger detection and evententity recognition is proposed to tackle Chinese EER task. We conduct extensive experimentson a real-world Chinese EER dataset. Compared with the previous methods TAM outperformsthe existing technical alternatives in terms of F1 measure. Besides TAM can accurately identifythe synonymous expressions that are not included in the trigger dictionary. Morover TAM canobtain a robust performance when only a few labeled triggers are available.</abstract>
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%0 Conference Proceedings
%T A Trigger-Aware Multi-Task Learning for Chinese Event Entity Recognition
%A Yangxiao, Xiang
%A Chenliang, Li
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G English
%F yangxiao-chenliang-2021-trigger
%X 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 namedentity 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 efficiency of the event trigger detection which have to require the tedious yet expensive human la-beling of the event triggers especially for languages where triggers contain multiple tokens andhave numerous synonymous expressions (such as Chinese). In this paper a novel trigger-awaremulti-task learning framework (TAM) which jointly performs both trigger detection and evententity recognition is proposed to tackle Chinese EER task. We conduct extensive experimentson a real-world Chinese EER dataset. Compared with the previous methods TAM outperformsthe existing technical alternatives in terms of F1 measure. Besides TAM can accurately identifythe synonymous expressions that are not included in the trigger dictionary. Morover TAM canobtain a robust performance when only a few labeled triggers are available.
%U https://aclanthology.org/2021.ccl-1.100
%P 1121-1130
Markdown (Informal)
[A Trigger-Aware Multi-Task Learning for Chinese Event Entity Recognition](https://aclanthology.org/2021.ccl-1.100) (Yangxiao & Chenliang, CCL 2021)
ACL