Paper: LASIGE: using Conditional Random Fields and ChEBI ontology

ACL ID S13-2109
Title LASIGE: using Conditional Random Fields and ChEBI ontology
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

For participating in the SemEval 2013 chal- lenge of recognition and classification of drug names, we adapted our chemical en- tity recognition approach consisting in Condi- tional Random Fields for recognizing chemi- cal terms and lexical similarity for entity res- olution to the ChEBI ontology. We obtained promising results, with a best F-measure of 0.81 for the partial matching task when us- ing post-processing. Using only Conditional Random Fields the results are slightly lower, achieving still a good result in terms of F- measure. Using the ChEBI ontology allowed a significant improvement in precision (best precision of 0.93 in partial matching task), which indicates that taking advantage of an ontology can be extremely useful for enhanc- ing chemical entity recognition.