Sam Scott, Stan Matwin , 1999, "Feature Engineering for text classification". In Ivan Bratko and Saso Dzeroski, editors, Proceedings of ICML-99, 16th International Conference on Machine Learning, San Francisco, pages 379388. Home/Search Document Details and Download
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Integrating Lexical Knowledge in Learning-Based.. - Hidalgo.. (Correct)
....[21] With the increasing availability of electronically accessible information, integrating other knowledge sources in ATC is receiving more and more attention. For instance, some people are interested on taking advantage of the lexical knowledge present in Lexical Databases (LDBs) like WordNet [10, 20, 2]. Others try to integrate heuristic knowledge available in specific domains like in spam messages categorization [6, 18] Also unlabelled documents have been used in ATC [15] We have focused on designing methods for integrating lexical knowledge extracted from LDBs. In this paper, we present a ....
....that guide our work is that the more informed a system is, the better it will perform. Several methods have been proposed for integrating lexical information to the training based approach in ATC. As far as we know, and apart of our previous work [2, 5, 3, 23] only the work by Scott and Matwin [20] and by Junker and Abecker [10] has focused in this task. Scott and Matwin propose to include WordNet information at the feature level. Their work is based on expanding each word in the training collection with all the synonyms extracted from WordNet for it, including those available for each ....
Sam Scott and Stan Matwin. Feature engineering for text classification. In Ivan Bratko and Saso Dzeroski, editors, Proceedings of ICML-99, 16th International Conference on Machine Learning, pages 379--388, Bled, SL, 1999. Morgan Kaufmann Publishers, San Francisco, US.
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....independence model [7] It has been shown that for text categorization applications, the multinomial model is most often the best choice [7, 16] therefore we will only consider Attributes are also called features in many papers. Feature selection is an important procedure in many classifiers [17, 24]. the multinomial naive Bayes model in this paper. Fig. 1 gives a graphical representation of the multinomial naive Bayes model, showing that each attribute node is independent of the other attributes given the class label C. C . Fig. 1. Graphical model of a naive Bayes classifier The ....
S. Scott and S. Matwin. (1999). Feature Engineering for Text Classification. In Proceedings of ICML' 99, pp. 379-388.
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Sam Scott, Stan Matwin , 1999, "Feature Engineering for text classification". In Ivan Bratko and Saso Dzeroski, editors, Proceedings of ICML-99, 16th International Conference on Machine Learning, San Francisco, pages 379388.
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S. Scott and S. Matwin. (1999). Feature engineering for text classification. Proc. of 16th International Conference on Machine Learning, Bled, Slovenia.
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Sam Scott and Stan Matwin. 1999. Feature engineering for text classification. In Proceedings of ICML'99, Bled, SL. Morgan Kaufmann Publishers, San Francisco, US.
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Machine Learning: Proc. 16th Int. Conf., pp. 379-388, 1999.
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S. Scott and S. Matwin. Feature engineering for text classification. In I. Bratko and S. Dzeroski, editors, Proceedings of 16th International Conference on Machine Learning (ICML-99), pages 379--388, Bled, SL, 1999. Morgan Kaufmann Publishers, San Francisco, US.
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Scott, S., & Matwin, S. (1999). "Feature engineering for text classification", in Machine Learning, Proceedings of the 16th International Conference (ICML'99), Bled, Slovenia, pp. 379-388.
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