Cargando…
A Relation-Oriented Model With Global Context Information for Joint Extraction of Overlapping Relations and Entities
The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal w...
Autores principales: | Han, Huihui, Wang, Jian, Wang, Xiaowen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290867/ https://www.ncbi.nlm.nih.gov/pubmed/35859657 http://dx.doi.org/10.3389/fnbot.2022.914705 |
Ejemplares similares
-
A Relational Adaptive Neural Model for Joint Entity and Relation Extraction
por: Duan, Guiduo, et al.
Publicado: (2021) -
RTJTN: Relational Triplet Joint Tagging Network for Joint Entity and Relation Extraction
por: Yang, Zhenyu, et al.
Publicado: (2021) -
Span-based single-stage joint entity-relation extraction model
por: Han, Dongchen, et al.
Publicado: (2023) -
A neural joint model for entity and relation extraction from biomedical text
por: Li, Fei, et al.
Publicado: (2017) -
Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning
por: Feng, Yuntian, et al.
Publicado: (2017)