Open AccessA Survey Deep Learning Based Relation Extraction
Author(s) -
Zhang Xiaxia,
Yugang Dai,
Tao Jiang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/3/032029
Subject(s) - relationship extraction , computer science , artificial intelligence , information extraction , relation (database) , natural language processing , mainstream , artificial neural network , margin (machine learning) , matching (statistics) , natural language , natural language understanding , data science , machine learning , data mining , philosophy , statistics , theology , mathematics
From human understanding of natural language to machine understanding of natural language, NLP has become the mainstream technology in the world.Extracting useful information from massive information, namely information extraction (IE), such as relation extraction (RE), is one of the important semantic processing tasks.With the explosion of web texts and the emergence of new relations, the margin of human knowledge has increased dramatically, and unstructured text processing has become a problem that needs to be overcome, so people have been working on RE for many years. From the earliest pattern matching to the current popular neural network, RE has made significant progress. After reviewing the development history of relation extraction, this paper analyzes and discuss the development of existing neural network models and pre-trained language models from static technology to dynamic technology and reinforcement learning; finally, combined with the latest development of NLP research technology, the future research direction and trend of entity relation extraction are prospected.