Abstract
BackgroundThe functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of research in the life sciences. To extract knowledge from the extensive literatures on such compounds and drugs, the organizers of BioCreative IV administered the CHEMical Compound and Drug Named Entity Recognition (CHEMDNER) task to establish a standard dataset for evaluating state-of-the-art chemical entity recognition methods.MethodsThis study introduces the approach of our CHEMDNER system. Instead of emphasizing the development of novel feature sets for machine learning, this study investigates the effect of various tag schemes on the recognition of the names of chemicals and drugs by using conditional random fields. Experiments were conducted using combinations of different tokenization strategies and tag schemes to investigate the effects of tag set selection and tokenization method on the CHEMDNER task.ResultsThis study presents the performance of CHEMDNER of three more representative tag schemes-IOBE, IOBES, and IOB12E-when applied to a widely utilized IOB tag set and combined with the coarse-/fine-grained tokenization methods. The experimental results thus reveal that the fine-grained tokenization strategy performance best in terms of precision, recall and F-scores when the IOBES tag set was utilized. The IOBES model with fine-grained tokenization yielded the best-F-scores in the six chemical entity categories other than the "Multiple" entity category. Nonetheless, no significant improvement was observed when a more representative tag schemes was used with the coarse or fine-grained tokenization rules. The best F-scores that were achieved using the developed system on the test dataset of the CHEMDNER task were 0.833 and 0.815 for the chemical documents indexing and the chemical entity mention recognition tasks, respectively.ConclusionsThe results herein highlight the importance of tag set selection and the use of different tokenization strategies. Fine-grained tokenization combined with the tag set IOBES most effectively recognizes chemical and drug names. To the best of the authors' knowledge, this investigation is the first comprehensive investigation use of various tag set schemes combined with different tokenization strategies for the recognition of chemical entities.
Highlights
The functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of research in the life sciences
The dataset consists of 10,000 abstracts and a total of 84,355 mentions of chemical compounds and drugs that had been manually labelled by domain experts
Seven categories of chemical entities adapted from the work of R Klinger, C Kolarik, J Fluck, M Hofmann-Apitius and CM Friedrich [3] were annotated in the corpus: (1) SYSTEMATIC: the systematic names, such as International Union of Pure and Applied Chemistry (IUPAC); (2) IDENTIFIERS: database IDs, including CAS numbers, PubChem IDs, company registry numbers, ChEBI and CHEMBL IDs; (3) FORMULA: molecular formula, SMILES, InChI, or InChIKey; (4) TRIVAL: trivial, brand, common or generic names of compounds; (5) FAMILY: chemical families that can be associated to chemical structures; (6) MULTIPLE: mentions that correspond to chemicals that are not described by a continuous string of characters; (7) ABBREVIATION: abbreviations and acronyms
Summary
The functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of research in the life sciences. Studies on the effects of chemical and drug on organismal growth and development under various conditions are very valuable As a result, both the academia and industry are interesting in finding new ways to retrieve and access chemical compound and drug-related information from narrative texts in a manner that minimizes methods of identifying chemical entities in articles and associating them to databases are no longer suffice to meet the needs of researchers, motivating the development of several chemical entity recognition approaches that are based on natural language processing approaches [2,3]. To accelerate the research into CHEMical Compound and Drug Name Entity Recognition (CHEMDNER), a CHEMDNER task was set by BioCreative IV [7] to improve the efficiency and accuracy of chemical and drug recognition, to the benefit of both academia and industry
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