Named entity recognition (NER) systems are often based on machine learning techniques to reduce the labor-intensive development 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 features, 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 be achieved with the former system. Still, our experiments show that using domain-specific features outperforms this general approach. Our system ranked first in the SemEval-2013 Task 9.1: Recognition and classification of pharmacological substances.
For example, the WBI-NER system that ranked first in the SemEval-2013 Task 9.1 (Recognition and classification of pharmacological substances, DNR), REF is based on a linearchain CRF with specialized features.
WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs
*SEM
Computer Science