Paper: WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs

ACL ID S13-2058
Title WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

Named entity recognition (NER) systems are often based on machine learning tech- niques to reduce the labor-intensive devel- opment of hand-crafted extraction rules and domain-dependent dictionaries. Nevertheless, time-consuming feature engineering is often needed to achieve state-of-the-art performance. In this study, we investigate the impact of such domain-specific features on the performance of recognizing and classifying mentions of pharmacological substances. We compare the performance of a system based on general fea- tures, which have been successfully applied to a wide range of NER tasks, with a system that additionally uses features generated from the output of an existing chemical NER tool and a collection of domain-specific resources. We demonstrate that acceptable results can ...