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FINTAL
2006

Incorporating External Information in Bayesian Classifiers Via Linear Feature Transformations

11 years 9 months ago
Incorporating External Information in Bayesian Classifiers Via Linear Feature Transformations
Naive Bayes classifier is a frequently used method in various natural language processing tasks. Inspired by a modified version of the method called the flexible Bayes classifier, we explore the use of linear feature transformations together with the Bayesian classifiers, because it provides us an elegant way to endow the classifier with an external information that is relevant to the task. While the flexible Bayes classifier is based on the idea of using kernel density estimation to obtain the class conditional probabilities of continuously valued attributes, we use the linear transformations to smooth the feature frequency counts of discrete valued attributes. We evaluate the method on the context sensitive spelling error correction problem using the Reuters corpus. For this particular task, we define a positional feature transformation and a word feature transformation that take advantage of the positional information of the context words and the part-of-speech information of words,...
Tapio Pahikkala, Jorma Boberg, Aleksandr Myllä
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where FINTAL
Authors Tapio Pahikkala, Jorma Boberg, Aleksandr Mylläri, Tapio Salakoski
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