Towards a mixed approach to extract biomedical terms from text corpus

Juan Antonio Lossio-Ventura 1, 2, * Clement Jonquet 2 Mathieu Roche 1 Maguelonne Teisseire 1
* Corresponding author
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 SMILE - Système Multi-agent, Interaction, Langage, Evolution
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The proposed work aims at automatically extracting biomedical terms from free text. We present new extraction methods taking into account linguistic patterns specialized for the biomedical field, statistic term extraction measures such as C-value and statistic keyword extraction measures such as Okapi BM25, and TFIDF. These measures are combined in order to improve the extraction process and we investigate which combinations are the more relevant associated to different contexts. Experimental results show that an appropriate harmonic mean of C-value associated to keyword extraction measures offers better precision, both for single-word and multi-words term extraction. Experiments describe the extraction of English and French biomedical terms from a corpus of laboratory tests available online. The results are validated by using UMLS (in English) and only MeSH (in French) as reference.
Keywords : Statistic Measure Text Mining Biomedical Natural Language Processing (BioNLP) Biomedical Term Extraction Biomedical Thesaurus
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Journal articles
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00859846
Contributor : Juan Antonio Lossio Ventura <>
Submitted on : Monday, July 7, 2014 - 3:29:52 PM
Last modification on : Tuesday, April 16, 2019 - 4:24:47 PM
Long-term archiving on: Tuesday, October 7, 2014 - 4:51:27 PM

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CNRS | LIRMM | SMILE | ADVANSE | MIPS | UNIV-MONTPELLIER

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Juan Antonio Lossio-Ventura, Clement Jonquet, Mathieu Roche, Maguelonne Teisseire. Towards a mixed approach to extract biomedical terms from text corpus. International journal of Knowledge Discovery in Bioinformatics, IGI Global, 2014, 4 (1), pp.1-15. ⟨10.4018/ijkdb.2014010101⟩. ⟨lirmm-00859846v2⟩

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