Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Named Entity Extraction using AdaBoost
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
POSBIOTM-NER in the shared task of BioNLP/NLPBA 2004
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Instance pruning by filtering uninformative words: an information extraction case study
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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In this paper we propose Instance Filtering as preprocessing step for supervised classification-based learning systems for entity recognition. The goal of Instance Filtering is to reduce both the skewed class distribution and the data set size by eliminating negative instances, while preserving positive ones as much as possible. This process is performed on both the training and test set, with the effect of reducing the learning and classification time, while maintaining or improving the prediction accuracy. We performed a comparative study on a class of Instance Filtering techniques, called Stop Word Filters, that simply remove all the tokens belonging to a list of stop words. We evaluated our approach on three different entity recognition tasks (i.e. Named Entity, Bio-Entity and Temporal Expression Recognition) in English and Dutch, showing that both the skewness and the data set size are drastically reduced. Consequently, we reported an impressive reduction of the computation time required for training and classification, while maintaining (and sometimes improving) the prediction accuracy.