DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT

Applied Sciences

OkRan Jeong¹  SungMin Yang¹  SoYeop Yoo¹ 

[1] Department of Software, Gachon University, Seongnam-si 13120, Korea;

关键词: named entity recognition;    relation extraction;    DQN;    BERT;    knowledge graph;   

DOI  :  10.3390/app10186429

来源: DOAJ

【 摘 要 】

Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction task, which is considered to be the most important in understanding sentences. We propose DeNERT-KG, a model that can extract subject, object, and relationships, to grasp the meaning inherent in a sentence. Based on the BERT language model and Deep Q-Network, the named entity recognition (NER) model for extracting subject and object is established, and a knowledge graph is applied for relation extraction. Using the DeNERT-KG model, it is possible to extract the subject, type of subject, object, type of object, and relationship from a sentence, and verify this model through experiments.

【 授权许可】

Unknown