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} } |