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Optimization of hierarchical reinforcement learning relationship extraction model

机译：优化分层强化学习关系提取模型

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摘要

Purpose - Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation. Design/methodology/approach - This paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model. Findings - Experiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction. Originality/value - The research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.
机译：目的——实体关系抽取是一个重要研究方向获得结构化信息。确定实体之间的关系吗在一个给定的句子基于分段法,很少考虑到一个实体和关系统一的框架。一个结合了关系和最优解实体。分层强化学习和框架提供一个有效的模型来提取实体关系。论文是基于分层强化学习框架的共同学习和结合伯特的模型,最好的语言表示模型,来优化这个词嵌入和编码的过程。调整一些标点符号的数据集更标准化,和介绍位置信息来提高性能的模型。本文提出的模型优于基线模型提高了13%,在F1的分数达到0.742 NYT10数据集。模型可以有效地提取实体和在大规模非结构化文本和关系可以应用于多域的领域吗信息检索、智能的理解和智能交互。这项研究提供了一个有效的解决方案研究人员在不同的领域使用人工智能(AI)技术更准确地处理他们的非结构化文本。

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