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- Title
Survey of Supervised Joint Entity Relation Extraction Methods.
- Authors
ZHANG Shaowei; WANG Xin; CHEN Zirui; WANG Lin; XU Dawei; JIA Yongzhe
- Abstract
As a core task of information extraction, joint entity relation extraction can automatically identify entities, the types of entities and the specific relation between entities from unstructured texts or semi-structured texts, which provides basic support for downstream tasks such as knowledge graph construction, intelligent question answering, semantic search, etc. The traditional pipeline method decomposes joint entity relation extraction into two independent subtasks, named entity recognition and relation extraction. Due to the lack of interaction between the two subtasks, there are some problems such as error propagation in pipeline method. Recently, joint entity relation extraction has become a new trend, since it can further improve the performance of the model by establishing a unified model and making different subtasks interact. The supervised joint entity relation extraction approaches are surveyed in this paper. According to different ways of extracting features, there are two categories of joint entity relation extraction approaches, i.e., joint extraction based on feature engineering and joint extraction based on neural network. Firstly, the joint extraction based on feature engineering is introduced, including integer linear programming, card pyramid parsing, probabilistic graphical model and structured prediction, all of these four methods need to adopt complex feature engineering methods. Secondly, the joint extraction based on neural network is presented, which can automatically extract the feature information, gradually becoming the mainstream methods of joint extraction. Parameter sharing methods and joint decoding methods are two kinds of joint extraction methods based on neural network. Thirdly, seven common datasets and evaluation metrics of the supervised joint entity relation extraction are described, and the experimental comparison and analysis of different joint entity relation extraction methods are conducted. Finally, the future research directions of the joint entity relation extraction are put forward.
- Subjects
KNOWLEDGE graphs; DATA mining; LINEAR programming; METHODS engineering; INTEGER programming
- Publication
Journal of Frontiers of Computer Science & Technology, 2022, Vol 16, Issue 4, p713
- ISSN
1673-9418
- Publication type
Article
- DOI
10.3778/j.issn.1673-9418.2107114