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        <title>UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction
%A Xie, ShuYi
%A Ma, Jian
%A Yang, Haiqin
%A Lianxin, Jiang
%A Yang, Mo
%A Shen, Jianping
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F xie-etal-2020-unixlong
%X 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.
%R 10.18653/v1/2020.semeval-1.96
%U https://aclanthology.org/2020.semeval-1.96
%U https://doi.org/10.18653/v1/2020.semeval-1.96
%P 730-736

Markdown (Informal)

[UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction](https://aclanthology.org/2020.semeval-1.96) (Xie et al., SemEval 2020)

• UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction (Xie et al., SemEval 2020)

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.