Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recognition

Mention recognition in chemical texts plays an important role in a wide-spread range of application areas. Feature selection and parameter optimization are the two important issues in machine learning. While the former improves the quality of a classifier by removing the redundant and irrelevant features, the later concerns finding the most suitable parameter values, which have significant impact on the overall classification performance. In this paper we formulate a joint model that performs feature selection and parameter optimization simultaneously, and propose two approaches based on the concepts of single and multiobjective optimization techniques. Classifier ensemble techniques are also employed to improve the performance further. We identify and implement variety of features that are mostly domain-independent. Experiments are performed with various configurations on the benchmark patent and Medline datasets. Evaluation shows encouraging performance in all the settings.