Summary of the paper

Title Maximum Entropy Classifier Ensembling using Genetic Algorithm for NER in Bengali
Authors Asif Ekbal and Sriparna Saha
Abstract In this paper, we propose classifier ensemble selection for Named Entity Recognition (NER) as a single objective optimization problem. Thereafter, we develop a method based on genetic algorithm (GA) to solve this problem. Our underlying assumption is that rather than searching for the best feature set for a particular classifier, ensembling of several classifiers which are trained using different feature representations could be a more fruitful approach. Maximum Entropy (ME) framework is used to generate a number of classifiers by considering the various combinations of the available features. In the proposed approach, classifiers are encoded in the chromosomes. A single measure of classification quality, namely F-measure is used as the objective function. Evaluation results on a resource constrained language like Bengali yield the recall, precision and F-measure values of 71.14%, 84.07% and 77.11%, respectively. Experiments also show that the classifier ensemble identified by the proposed GA based approach attains higher performance than all the individual classifiers and two different conventional baseline ensembles.
Topics Named Entity recognition, Statistical and machine learning methods, Other
Full paper Maximum Entropy Classifier Ensembling using Genetic Algorithm for NER in Bengali
Slides Maximum Entropy Classifier Ensembling using Genetic Algorithm for NER in Bengali
Bibtex @InProceedings{EKBAL10.718,
  author = {Asif Ekbal and Sriparna Saha},
  title = {Maximum Entropy Classifier Ensembling using Genetic Algorithm for NER in Bengali},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
 }
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