Feature Selection Using Multiobjective Optimization for Named Entity Recognition

Appropriate feature selection is a very crucial issue in any machine learning framework, specially in Maximum Entropy (ME). In this paper, the selection of appropriate features for constructing a ME based Named Entity Recognition (NER) system is posed as a multiobjective optimization (MOO) problem. Two classification quality measures, namely recall and precision are simultaneously optimized using the search capability of a popular evolutionary MOO technique, NSGA-II. The proposed technique is evaluated to determine suitable feature combinations for NER in two languages, namely Bengali and English that have significantly different characteristics. Evaluation results yield the recall, precision and

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Asif Ekbal, Sriparna Saha
• Keywords: Appropriate Feature Selection, Appropriate Features, Computer Vision, Evolutionary Moo Technique, ICPR 2010