title: Chemical entity recognition in patents by combining dictionary-based and statistical approaches.
abstract: We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents.
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam.
authors.forename: Saber A
authors.initials: SA
authors.lastname: Akhondi
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam.
authors.forename: Ewoud
authors.initials: E
authors.lastname: Pons
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam.
authors.forename: Zubair
authors.initials: Z
authors.lastname: Afzal
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam.
authors.forename: Herman
authors.initials: H
authors.lastname: van Haagen
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam.
authors.forename: Benedikt F H
authors.initials: BF
authors.lastname: Becker
authors.affiliation: Department of Human Genetics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands.
authors.forename: Kristina M
authors.initials: KM
authors.lastname: Hettne
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam.
authors.forename: Erik M
authors.initials: EM
authors.lastname: van Mulligen
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam j.kors@erasmusmc.nl.
authors.forename: Jan A
authors.initials: JA
authors.lastname: Kors
mesh_terms: D057225:Data Mining / Q000379:methods*; D062126:Databases, Chemical; D000069550:Machine Learning; D015233:Models, Statistical; D010330:Patents as Topic; D012984:Software