Open AccessAn Overview of Entity Relation Extraction
Author(s) -
Yixuan Xie
Publication year - 2021
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 - 10.1088/1742-6596/1827/1/012146
Subject(s) - relationship extraction , relation (database) , computer science , embedding , information extraction , extraction (chemistry) , word embedding , word (group theory) , artificial intelligence , shot (pellet) , natural language processing , information retrieval , data mining , mathematics , chemistry , geometry , organic chemistry , chromatography
Plenty of digital text is generated and shared in nowadays life, which constitutes many unstructured text resources. An automatically information extraction method is highly in demand as a large amount of useful information can be stored and output through it. In this paper, several basic concepts about entity relation extraction are introduced, including word embedding, positional embedding and convolutional neutral networks. Several types of relation extraction are also discussed: supervised relation extraction, relation extraction using distant supervision and relation extraction using few-shot approach. In addition, the existing challenges and problems are discussed, like overlapped triples, wrong labeled data and the difficulties that few-shot learning approaches are now facing.