Title
Gimli: open source and high-performance biomedical name recognition.
Abstract
Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research.We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions.Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli.
Year
DOI
Venue
2013
10.1186/1471-2105-14-54
BMC Bioinformatics
Keywords
Field
DocType
bioinformatics,microarrays,algorithms
Command-line interface,Data science,Conditional random field,Computer science,Usability,Biomedical text mining,Software,Information extraction,Bioinformatics,Personalization
Journal
Volume
Issue
ISSN
14
1
1471-2105
Citations 
PageRank 
References 
53
1.54
21
Authors
3
Name
Order
Citations
PageRank
David Campos121910.69
Sérgio Matos241529.51
José Luis Oliveira376084.03