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

机译:Semeval-2020的Unixlong任务6:定义提取的联合模型

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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 TAGJD, 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.
机译:定义提取是从文本中自动提取条款及其定义的任务。近年来,它吸引了NLP研究人员的广泛兴趣。本文介绍了Unixlong Team的Semeval Task 6:Defteval:在自由文本中提取术语定义对。此任务的目标是提取定义,单词级生物标签和关系。由于文本的自由风格,这项任务是挑战,特别是术语中术语范围的定义,并且缺乏显式动词短语。我们提出了一个联合模型,以培训定义提取的任务和同时的单词级别标记。我们设计了伯特的创意格式输入,以捕获实体之间的位置信息及其定义。然后我们用一些规则调整BERT的结果。最后,我们应用TagJD,Root_ID,BIO标记来预测关系并实现宏观平均的F1得分1.0,在关系提取子任务中的官方测试中排名第一。

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