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What makes a gene name? Named entity recognition in the biomedical literature [PDF]

Briefings in Bioinformatics, 2005
The recognition of biomedical concepts in natural text (named entity recognition, NER) is a key technology for automatic or semi-automatic analysis of textual resources. Precise NER tools are a prerequisite for many applications working on text, such as information retrieval, information extraction or document classification.
Ulf Leser, Jörg Hakenberg
exaly   +3 more sources

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MMBERT: a unified framework for biomedical named entity recognition

Medical & Biological Engineering & Computing, 2023
Named entity recognition (NER) is an important task in natural language processing (NLP). In recent years, NER has attracted much attention in the biomedical field. However, due to the lack of biomedical named entity identification datasets, the complexity and rarity of biomedical named entities and so on, biomedical NER is more difficult than general ...
Lei Fu, Zuquan Weng, Jiheng Zhang, Haihe Xie, Yiqing Cao   +4 more
openaire   +2 more sources

Feature Importance for Biomedical Named Entity Recognition

2019
Within the domain of biomedical natural language processing (bioNLP), researchers have used many token features for machine learning models. With recent progress in word embeddings algorithms, it is no longer clear if most of these features are still useful.
Hamish Huggard, Aaron Zhang, Edmond Zhang, Yun Sing Koh   +3 more
openaire   +1 more source

Bidirectional LSTM-CRF for biomedical named entity recognition

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2018
Bio-medical entity recognition extracts significant entities, for instance cells, proteins and genes, which is an arduous task in an automatic system that mine knowledge in bioinformatics texts. In this thesis, we utilized a bidirectional long short-term memory (Bi-LSTM) combined with conditional random fields (CRFs) approach to automatically obtain ...
Xuemin Yang, Zhihong Gao, Yongmin Li 0001, Chuandi Pan, Ronggen Yang, Lejun Gong, Geng Yang   +6 more
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Classifier subset selection for biomedical named entity recognition

Applied Intelligence, 2008
Classifier ensembling approach is considered for biomedical named entity recognition task. A vote-based classifier selection scheme having an intermediate level of search complexity between static classifier selection and real-valued and class-dependent weighting approaches is developed.
Dimililer, Nazife, Varoglu, Ekrem, Altincay, Hakan   +2 more
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Biomedical Named Entity Recognition with Tri-Training Learning

2009 2nd International Conference on Biomedical Engineering and Informatics, 2009
In order to solve the data scarcity problem, this paper presented a co-training style method for Biomedical Named Entity Recognition. We proposed a novel selection method for tri-training learning, using three classifiers: CRFs,SVMs and ME. In tri-training process, we select new newly labeled samples based on the selection model maximizing training ...
YueHong Cai, XianYi Cheng
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ATRMiner : A system for automatic biomedical named entities recognition

2010 Sixth International Conference on Natural Computation, 2010
The recognition of biomedical entities in natural text is a key step for automatic analysis of textual resources. Biomedical entities recognition tools are a prerequisite for many applications working on text. In this paper, we develop a biomedical entities recognition tool named ATRMiner.
Lejun Gong, Xiao Sun 0006
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Towards the Named Entity Recognition Methods in Biomedical Field

2020
Natural Language Processing (NLP) is very important in modern data processing taking into consideration different sources, forms and purpose of data as well as information in different areas our industry, administration, public and private life. Our studies concern Natural Language Processing techniques in biomedical field.
Anna Sniegula, Aneta Poniszewska-Maranda, Lukasz Chomatek   +2 more
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Biomedical Named Entity Recognition Based on MCBERT

2022 International Conference on Asian Language Processing (IALP), 2022
Sai Wang, Hankiz Yilahun, Askar Hamdulla
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Named entity recognition for tamil biomedical documents

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], 2014
Valuable Information about tamil traditional medicines are available in various forms like books, magazines and websites. These instructions are however very large and unstructured. Our system focuses on constructing a NER identification module using SVM classifier to identify named entities and to classify them into their corresponding categories. The
J. Betina Antony, G. S Mahalakshmi
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