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J Cheminform. 2015 Jan 19;7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S15. doi: 10.1186/1758-2946-7-S1-S15. eCollection 2015.

CheNER: a tool for the identification of chemical entities and their classes in biomedical literature.

Author information

1
Departament Ciències Mèdiques Bàsiques, Universitat de Lleida, Av. Rovira Roure nº 80, 25298 Lleida, Spain ; Departament d'Informàtica i Enginyeria Industrial, Universitat de Lleida, C/Jaume II nº 69, 25001, Lleida, Spain ; Centro de Biotecnologia Agricola e Agro-Alimentar do Baixo Alentejo (CEBAL), Rua. Pedro Soares s/n, Campus IPBeja, 6158 7801-908 Beja, Portugal.
2
Departament Ciències Mèdiques Bàsiques, Universitat de Lleida, Av. Rovira Roure nº 80, 25298 Lleida, Spain.
3
Departament d'Informàtica i Enginyeria Industrial, Universitat de Lleida, C/Jaume II nº 69, 25001, Lleida, Spain.

Abstract

BACKGROUND:

Small chemical molecules regulate biological processes at the molecular level. Those molecules are often involved in causing or treating pathological states. Automatically identifying such molecules in biomedical text is difficult due to both, the diverse morphology of chemical names and the alternative types of nomenclature that are simultaneously used to describe them. To address these issues, the last BioCreAtIvE challenge proposed a CHEMDNER task, which is a Named Entity Recognition (NER) challenge that aims at labelling different types of chemical names in biomedical text.

METHODS:

To address this challenge we tested various approaches to recognizing chemical entities in biomedical documents. These approaches range from linear Conditional Random Fields (CRFs) to a combination of CRFs with regular expression and dictionary matching, followed by a post-processing step to tag those chemical names in a corpus of Medline abstracts. We named our best performing systems CheNER.

RESULTS:

We evaluate the performance of the various approaches using the F-score statistics. Higher F-scores indicate better performance. The highest F-score we obtain in identifying unique chemical entities is 72.88%. The highest F-score we obtain in identifying all chemical entities is 73.07%. We also evaluate the F-Score of combining our system with ChemSpot, and find an increase from 72.88% to 73.83%.

CONCLUSIONS:

CheNER presents a valid alternative for automated annotation of chemical entities in biomedical documents. In addition, CheNER may be used to derive new features to train newer methods for tagging chemical entities. CheNER can be downloaded from http://metres.udl.cat and included in text annotation pipelines.

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