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 EntityRecognition (NER) as a single objective optimization problem. Thereafter, wedevelop a method based on genetic algorithm (GA) to solve this problem. Ourunderlying assumption is that rather than searching for the best feature setfor a particular classifier, ensembling of several classifiers which aretrainedusing different feature representations could be a more fruitful approach.Maximum Entropy (ME) framework is used to generate a number of classifiers byconsidering the various combinations of the available features.In the proposed approach, classifiers are encoded in the chromosomes. A singlemeasure of classification quality, namely F-measure is used as the objectivefunction. Evaluation results on a resource constrained language like Bengaliyield the recall, precision and F-measure values of 71.14%, 84.07% and 77.11%,respectively. Experiments also show that the classifier ensemble identified bythe proposed GA based approach attains higher performance than all theindividual classifiers and two different conventional baseline ensembles. |
| Language | Other |
| Topics | Named Entity recognition, Statistical and machine learning methods, Other |
Full paper  |
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 conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |