UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction

ShuYi Xie, Jian Ma, Haiqin Yang, Jiang Lianxin, Mo Yang, Jianping Shen


Abstract
Definition Extraction is the task to automatically extract terms and their definitions from text. In recent years, it attracts wide interest from NLP researchers. This paper describes the unixlong team’s system for the SemEval 2020 task6: DeftEval: Extracting term-definition pairs in free text. The goal of this task is to extract definition, word level BIO tags and relations. This task is challenging due to the free style of the text, especially the definitions of the terms range across several sentences and lack explicit verb phrases. We propose a joint model to train the tasks of definition extraction and the word level BIO tagging simultaneously. We design a creative format input of BERT to capture the location information between entity and its definition. Then we adjust the result of BERT with some rules. Finally, we apply TAG_ID, ROOT_ID, BIO tag to predict the relation and achieve macro-averaged F1 score 1.0 which rank first on the official test set in the relation extraction subtask.
Anthology ID:
2020.semeval-1.96
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
730–736
Language:
URL:
https://aclanthology.org/2020.semeval-1.96
DOI:
10.18653/v1/2020.semeval-1.96
Bibkey:
Cite (ACL):
ShuYi Xie, Jian Ma, Haiqin Yang, Jiang Lianxin, Mo Yang, and Jianping Shen. 2020. UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 730–736, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction (Xie et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.96.pdf
Data
DEFT Corpus