[PDF][PDF] Introduction to the bio-entity recognition task at JNLPBA
We describe here the JNLPBA shared task of bio-entity recognition using an extended
version of the GENIA version 3 named entity corpus of MEDLINE abstracts. We provide …
version of the GENIA version 3 named entity corpus of MEDLINE abstracts. We provide …
GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text
Motivation Best performing named entity recognition (NER) methods for biomedical literature
are based on hand-crafted features or task-specific rules, which are costly to produce and …
are based on hand-crafted features or task-specific rules, which are costly to produce and …
Gimli: open source and high-performance biomedical name recognition
Background Automatic recognition of biomedical names is an essential task in biomedical
information extraction, presenting several complex and unsolved challenges. In recent …
information extraction, presenting several complex and unsolved challenges. In recent …
Various criteria in the evaluation of biomedical named entity recognition
Background Text mining in the biomedical domain is receiving increasing attention. A key
component of this process is named entity recognition (NER). Generally speaking, two …
component of this process is named entity recognition (NER). Generally speaking, two …
ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature
Background The rapid growth of scientific literature has rendered the task of finding relevant
information one of the critical problems in almost any research. Search engines, like Google …
information one of the critical problems in almost any research. Search engines, like Google …
[HTML][HTML] Feature selection techniques for maximum entropy based biomedical named entity recognition
Named entity recognition is an extremely important and fundamental task of biomedical text
mining. Biomedical named entities include mentions of proteins, genes, DNA, RNA, etc …
mining. Biomedical named entities include mentions of proteins, genes, DNA, RNA, etc …
Named entity recognition from biomedical texts using a fusion attention-based BiLSTM-CRF
H Wei, M Gao, A Zhou, F Chen, W Qu, C Wang… - IEEE …, 2019 - ieeexplore.ieee.org
Biomedical named entity recognition (BNER) is the basis of biomedical text mining and one
of the core sub-tasks of information extraction. Previous BNER models based on …
of the core sub-tasks of information extraction. Previous BNER models based on …
Named entity recognition using word embedding as a feature
This study applied word embedding to feature for named entity recognition (NER) training,
and used CRF as a learning algorithm. Named entities are phrases that contain the names …
and used CRF as a learning algorithm. Named entities are phrases that contain the names …
Stacked ensemble coupled with feature selection for biomedical entity extraction
Entity extraction is one of the most fundamental and important tasks in biomedical
information extraction. In this paper we propose a two-stage algorithm for the extraction of …
information extraction. In this paper we propose a two-stage algorithm for the extraction of …
How to make the most of NE dictionaries in statistical NER
Background When term ambiguity and variability are very high, dictionary-based Named
Entity Recognition (NER) is not an ideal solution even though large-scale terminological …
Entity Recognition (NER) is not an ideal solution even though large-scale terminological …