Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison
The biomedical literature is growing rapidly, and the extraction of meaningful information
from the large amount of literature is increasingly important. Biomedical named entity …
from the large amount of literature is increasingly important. Biomedical named entity …
Machine learning techniques for biomedical natural language processing: a comprehensive review
EH Houssein, RE Mohamed, AA Ali - IEEE Access, 2021 - ieeexplore.ieee.org
The widespread use of electronic health records (EHR) systems in health care provides a
large amount of real-world data, leading to new areas for clinical research. Natural language …
large amount of real-world data, leading to new areas for clinical research. Natural language …
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Motivation In biomedical research, chemical is an important class of entities, and chemical
named entity recognition (NER) is an important task in the field of biomedical information …
named entity recognition (NER) is an important task in the field of biomedical information …
Learning named entity tagger using domain-specific dictionary
Recent advances in deep neural models allow us to build reliable named entity recognition
(NER) systems without handcrafting features. However, such methods require large …
(NER) systems without handcrafting features. However, such methods require large …
Biomedical entity representations with synonym marginalization
Biomedical named entities often play important roles in many biomedical text mining tools.
However, due to the incompleteness of provided synonyms and numerous variations in their …
However, due to the incompleteness of provided synonyms and numerous variations in their …
Collabonet: collaboration of deep neural networks for biomedical named entity recognition
Background Finding biomedical named entities is one of the most essential tasks in
biomedical text mining. Recently, deep learning-based approaches have been applied to …
biomedical text mining. Recently, deep learning-based approaches have been applied to …
Recent advances in biomedical literature mining
The recent years have witnessed a rapid increase in the number of scientific articles in
biomedical domain. These literature are mostly available and readily accessible in …
biomedical domain. These literature are mostly available and readily accessible in …
A neural multi-task learning framework to jointly model medical named entity recognition and normalization
State-of-the-art studies have demonstrated the superiority of joint modeling over pipeline
implementation for medical named entity recognition and normalization due to the mutual …
implementation for medical named entity recognition and normalization due to the mutual …
[HTML][HTML] A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature
Recently joint modeling methods of entity and relation exhibit more promising results than
traditional pipelined methods in general domain. However, they are inappropriate for the …
traditional pipelined methods in general domain. However, they are inappropriate for the …
Swellshark: A generative model for biomedical named entity recognition without labeled data
We present SwellShark, a framework for building biomedical named entity recognition
(NER) systems quickly and without hand-labeled data. Our approach views biomedical …
(NER) systems quickly and without hand-labeled data. Our approach views biomedical …