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1
ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents
In: Front Res Metr Anal (2021)
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2
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings ...
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3
[The Confusion Assessment Method: Transcultural adaptation of a French version]
In: ISSN: 0398-7620 ; Epidemiology and Public Health / Revue d'Epidémiologie et de Santé Publique ; https://hal.archives-ouvertes.fr/hal-02337685 ; Epidemiology and Public Health / Revue d'Epidémiologie et de Santé Publique, Elsevier Masson, 2018, 66 (3), pp.187--194. ⟨10.1016/j.respe.2018.01.133⟩ (2018)
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4
A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC
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5
Chemical entity recognition in patents by combining dictionary-based and statistical approaches
Akhondi, Saber A.; Pons, Ewoud; Afzal, Zubair. - : Oxford University Press, 2016
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6
Extraction of chemical-induced diseases using prior knowledge and textual information
Abstract: We describe our approach to the chemical–disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtasks: automatic disease-named entity recognition and normalization (DNER), and extraction of chemical-induced diseases (CIDs) from Medline abstracts. For the DNER subtask, we used our concept recognition tool Peregrine, in combination with several optimization steps. For the CID subtask, our system, which we named RELigator, was trained on a rich feature set, comprising features derived from a graph database containing prior knowledge about chemicals and diseases, and linguistic and statistical features derived from the abstracts in the CDR training corpus. We describe the systems that were developed and present evaluation results for both subtasks on the CDR test set. For DNER, our Peregrine system reached an F-score of 0.757. For CID, the system achieved an F-score of 0.526, which ranked second among 18 participating teams. Several post-challenge modifications of the systems resulted in substantially improved F-scores (0.828 for DNER and 0.602 for CID). RELigator is available as a web service at http://biosemantics.org/index.php/software/religator.
Keyword: Original Article
URL: https://doi.org/10.1093/database/baw046
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4831722/
http://www.ncbi.nlm.nih.gov/pubmed/27081155
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7
The CHEMDNER corpus of chemicals and drugs and its annotation principles
Krallinger, Martin; Rabal, Obdulia; Leitner, Florian. - : BioMed Central, 2015
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8
A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC ...
Kors, Jan A; Clematide, Simon; Akhondi, Saber A. - : BMJ Publishing Group, 2015
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9
A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC
Kors, Jan A; Clematide, Simon; Akhondi, Saber A. - : Oxford University Press, 2015
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10
Creating Multilingual Gold Standard Corpora for Biomedical Concept Recognition ...
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11
Creating Multilingual Gold Standard Corpora for Biomedical Concept Recognition
In: http://ceur-ws.org/Vol-1179/CLEF2013wn-CLEFER-KorsEt2013.pdf
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12
RESEARCH Open Access The CHEMDNER corpus of chemicals and drugs and its annotation principles
In: http://www.jcheminf.com/content/pdf/1758-2946-7-S1-S2.pdf
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