Welcome to the InfoSci Platform
Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text

Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text

Usha B. Biradar, Harsha Gurulingappa, Lokanath Khamari, Shashikala Giriyan
Copyright: © 2016 |Volume: 8 |Issue: 1 |Pages: 15
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781466690684|DOI: 10.4018/IJSSCI.2016010101
Cite Article Cite Article

MLA

Biradar, Usha B., et al. "Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text." IJSSCI vol.8, no.1 2016: pp.1-15. http://doi.org/10.4018/IJSSCI.2016010101

APA

Biradar, U. B., Gurulingappa, H., Khamari, L., & Giriyan, S. (2016). Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text. International Journal of Software Science and Computational Intelligence (IJSSCI), 8(1), 1-15. http://doi.org/10.4018/IJSSCI.2016010101

Chicago

Biradar, Usha B., et al. "Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text," International Journal of Software Science and Computational Intelligence (IJSSCI) 8, no.1: 1-15. http://doi.org/10.4018/IJSSCI.2016010101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Identification of chemical named entities in text and subsequent linkage of information to biological events is of immense value to fulfill the knowledge needs of pharmaceutical and chemical R&D. A significant amount of investigation has been carried out since a decade for identifying chemical named entities at morphological level. However, a barrier still remains in terms of value proposition to scientists at chemistry level. Therefore, the work described here aims to circumvent the information barrier by adaptation of a Conditional Random Fields-based approach for identifying chemical named entities at various levels namely generic chemical level, morphological level, and chemistry level. Substantial effort has been invested on generation of suitable multi-level annotated corpora. Recommended machine learning practices such as active learning-based training corpus generation and feature optimization have been systematically performed. Evaluation of system performance and benchmarking against the other state-of-the-approaches showed improved results.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.