BERT-BIGRU-CRF: A Novel Entity Relationship Extraction Model

Jianghai Lv, Junping Du, Nan Zhou, Zhe Xue
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引用次数: 2

Abstract

Entity name recognition and entity relationship extraction are the most critical foundation for building knowledge graph, and it is also the basic task of NPL. The main purpose of entity relationship extraction is to extract the semantic relationship between the pairs of marked entities in the sentence, that is, to determine the relationship categories between entity pairs in unstructured text based on entity identification, and to form structured data for storage and retrieval. This paper proposes a BERT-BIGRU-CRF entity relationship extraction method, which effectively changes the relationship between the pre-training generated word vector and the downstream specific NLP task, and gradually moves the downstream specific NLP task to the pre-training generated word vector. Our method achieves better performance of relationship extraction and entity name recognition, which helps to construct the knowledge graph more accurately.
一种新的实体关系抽取模型
实体名称识别和实体关系提取是构建知识图谱最关键的基础,也是不良资产管理的基本任务。实体关系提取的主要目的是提取句子中标记实体对之间的语义关系,即在实体识别的基础上确定非结构化文本中实体对之间的关系类别,形成结构化数据进行存储和检索。本文提出了BERT-BIGRU-CRF实体关系提取方法,该方法有效地改变了预训练生成的词向量与下游特定NLP任务之间的关系,将下游特定NLP任务逐步移动到预训练生成的词向量上。该方法在关系提取和实体名称识别方面取得了较好的性能,有助于更准确地构建知识图谱。
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