Hong-Jie Dai1, Po-Ting Lai2, Yung-Chun Chang3, Richard Tzong-Han Tsai4. 1. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. 2. Institute of Information Science, Academia Sinica, Taipei, Taiwan. 3. Institute of Information Science, Academia Sinica, Taipei, Taiwan ; Department of Information Management, National Taiwan University, Taipei, Taiwan. 4. Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.
Abstract
BACKGROUND: 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. 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. METHODS: This 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. RESULTS: This 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. CONCLUSIONS: The 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.
BACKGROUND: 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. 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. METHODS: This 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. RESULTS: This 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. CONCLUSIONS: The 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.
Authors: Roman Klinger; Corinna Kolárik; Juliane Fluck; Martin Hofmann-Apitius; Christoph M Friedrich Journal: Bioinformatics Date: 2008-07-01 Impact factor: 6.937
Authors: Alexander A Morgan; Zhiyong Lu; Xinglong Wang; Aaron M Cohen; Juliane Fluck; Patrick Ruch; Anna Divoli; Katrin Fundel; Robert Leaman; Jörg Hakenberg; Chengjie Sun; Heng-hui Liu; Rafael Torres; Michael Krauthammer; William W Lau; Hongfang Liu; Chun-Nan Hsu; Martijn Schuemie; K Bretonnel Cohen; Lynette Hirschman Journal: Genome Biol Date: 2008-09-01 Impact factor: 13.583
Authors: Larry Smith; Lorraine K Tanabe; Rie Johnson nee Ando; Cheng-Ju Kuo; I-Fang Chung; Chun-Nan Hsu; Yu-Shi Lin; Roman Klinger; Christoph M Friedrich; Kuzman Ganchev; Manabu Torii; Hongfang Liu; Barry Haddow; Craig A Struble; Richard J Povinelli; Andreas Vlachos; William A Baumgartner; Lawrence Hunter; Bob Carpenter; Richard Tzong-Han Tsai; Hong-Jie Dai; Feng Liu; Yifei Chen; Chengjie Sun; Sophia Katrenko; Pieter Adriaans; Christian Blaschke; Rafael Torres; Mariana Neves; Preslav Nakov; Anna Divoli; Manuel Maña-López; Jacinto Mata; W John Wilbur Journal: Genome Biol Date: 2008-09-01 Impact factor: 13.583