@inproceedings{ekbal-saha-2010-maximum,
    title = "Maximum Entropy Classifier Ensembling using Genetic Algorithm for {NER} in {B}engali",
    author = "Ekbal, Asif  and
      Saha, Sriparna",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Odijk, Jan  and
      Piperidis, Stelios  and
      Rosner, Mike  and
      Tapias, Daniel",
    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
    month = may,
    year = "2010",
    address = "Valletta, Malta",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-emnlp/L10-1496/",
    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."
}