Hierarchical Relation Extraction Based On Bidirectional Long Short-Term Memory Networks

Jia Chen, Liang Liu, Jiali Xu, Bei Hui
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Abstract

Relation extraction is an important task in the field of natural language processing (NLP). Most of the present methods extract each relation in isolation, without considering the hierarchical semantic information between relations. A novel loss function to optimize model of relation extraction based on hierarchical relation has been proposed in this paper. The experimental results show that the proposed model outperforms most of the present methods.
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基于双向长短期记忆网络的层次关系提取
关系提取是自然语言处理(NLP)领域的一项重要任务。目前的方法大多是孤立地提取每个关系,而没有考虑关系之间的层次语义信息。本文提出了一种新的损失函数来优化基于层次关系的关系抽取模型。实验结果表明,该模型的性能优于现有的大多数方法。
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