CrossRef Text and Data Mining
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Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data
Arnaud Ferré, Mouhamadou Ba, Robert Bossy
Genomics Inform. 2019;17(2):e20  Published online June 27, 2019
DOI: https://doi.org/10.5808/GI.2019.17.2.e20

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Improving methods for normalizing biomedical text entities with concepts from an ontology with (almost) no training data at BLAH5 the CONTES
Genomics & Informatics. 2019;17(2):e20   Crossref logo
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Jointly Extract Entities and Their Relations From Biomedical Text
IEEE Access. 2019;7:162818-162827   Crossref logo
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Leveraging MapReduce to efficiently extract associations between biomedical concepts from large text data
Microprocessors and Microsystems. 2016;46:202-210   Crossref logo
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Extraction and Representation of Financial Entities from Text
Data Science for Economics and Finance. 2021;241-263   Crossref logo
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Comparing breast cancer treatments using automatically detected surrogate and clinically relevant outcomes entities from text
Journal of Biomedical Informatics. 2019;100:100005   Crossref logo
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A method of inferring the relationship between Biomedical entities through correlation analysis on text
BioMedical Engineering OnLine. 2018;17(S2):   Crossref logo
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Ontology Learning and Population from Text
. 2006;   Crossref logo
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Ontology-based clinical information extraction from physician’s free-text notes
Journal of Biomedical Informatics. 2019;98:103276   Crossref logo
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Ontology of core data mining entities
Data Mining and Knowledge Discovery. 2014;28(5-6):1222-1265   Crossref logo
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Balanced undersampling: a novel sentence-based undersampling method to improve recognition of named entities in chemical and biomedical text
Applied Intelligence. 2017;48(8):1965-1978   Crossref logo
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