Cargando…
Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, w...
Autores principales: | Tang, Buzhou, Cao, Hongxin, Wang, Xiaolong, Chen, Qingcai, Xu, Hua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3963372/ https://www.ncbi.nlm.nih.gov/pubmed/24729964 http://dx.doi.org/10.1155/2014/240403 |
Ejemplares similares
-
Improving deep learning method for biomedical named entity recognition by using entity definition information
por: Xiong, Ying, et al.
Publicado: (2021) -
Feature Engineering for Drug Name Recognition in Biomedical Texts: Feature Conjunction and Feature Selection
por: Liu, Shengyu, et al.
Publicado: (2015) -
Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations
por: Munkhdalai, Tsendsuren, et al.
Publicado: (2015) -
Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
por: Tang, Buzhou, et al.
Publicado: (2013) -
CNN-based ranking for biomedical entity normalization
por: Li, Haodi, et al.
Publicado: (2017)