In this paper, we investigate how effective the use of semantic infor-mation could be in text categorization tasks. To this end, we consider distinct representations of documents differing in the kind of information incorporated: (a) information about terms only, (b) semantic information, and (c) a

Attempts to use natural language processing (NLP) for text categorization and information retrieval (IR) have had mixed results. Nevertheless, there is a strong intuition that NLP is important, at least for some tasks. In this paper, we discuss a task involving captioned images for which

Abstract. Improvement in the accuracy of identifying the correct word sense (WSD) will give better results for many natural language processing tasks. In this paper, we present a new approach using WSD as an aid for Text Categorization (TC). This approach integrates a set of linguistics resources

This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text

in the Rocchio algorithm, particularly the word weighting scheme and the similarity metric. It also suggests improvements which lead to a probabilistic variant of the Rocchio classifier. The Rocchio classifier, its probabilistic variant, and a naive Bayes classifier are compared on six text categorization tasks

Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors

was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness. 1 Introduction Text classification is the automated

ABSTRACT Text categorization -the assignment of natural language texts to one or more predefined categories based on their content -is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text

This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach