In the last years the quantity of text documents is increasing continually and automatic document classification is an important challenge. In the text document classification the training step is essential in obtaining a good classifier. The quality of learning depends on the dimension of the training data. When working with huge learning data sets, problems regarding the training time that in...

There are three factors involved in text classification. These are classification model, similarity measure and document representation model. In this paper, we will focus on document representation and demonstrate that the choice of document representation has a profound impact on the quality of the classifier. In our experiments, we have used the centroid-based text classifier, which is a sim...

In the last years the quantity of text documents is increasing continually and automatic document classification is an important challenge. In the text document classification the training step is essential in obtaining good results. The quality of learning depends on the dimension of the training data. When working with huge learning data sets, problems regarding the training time that increas...

There has been a lot of research targeting text classification. Many of them focus on a particular characteristic of text data multi-labelity. This arises due to the fact that a document may be associated with multiple classes at the same time. The consequence of such a characteristic is the low performance of traditional binary or multi-class classification techniques on multi-label text data....

Text Mining has become an important research area, which refers to the application of machine learning (or data mining) techniques in the study of Information Retrieval and Natural Language Processing. In sense, it is defined as the way of discovering knowledge from ubiquitous text data which are easily accessible over the Internet or the Intranet. The survey of Text Mining techniques, Text Min...

Text Classification, also known as text categorization, is the task of automatically allocating unlabeled documents into predefined categories. Text Classification means allocating a document to one or more categories or classes. The ability to accurately perform a classification task depends on the representations of documents to be classified. Text representations transform the textural docum...

Text Classification, also known as text categorization, is the task of automatically allocating unlabeled documents into predefined categories. Text Classification means allocating a document to one or more categories or classes. The ability to accurately perform a classification task depends on the representations of documents to be classified. Text representations transform the textural docum...

Current representation schemes for automatic text classification treat documents as syntactically unstructured collections of words or ‘concepts’. Past attempts to encode syntactic structure have treated part-of-speech information as another word-like feature, but have been shown to be less effective than non-structural approaches. We propose a new representation scheme using Holographic Reduce...

Text classification is a supervised technique that uses labeled training data to learn the classification system and then automatically classifies the remaining text using the learned system. Classification plays a vital role in many information management and retrieval tasks. Classification includes different parts such as text processing, feature extraction, feature vector construction and fi...

INTRODUCTION Document classification developed over the last ten years, using techniques originating from the pattern recognition and machine learning communities. All these methods do operate on flat text representations where word occurrences are considered independents. The recent paper (Sebastiani, 2002) gives a very good survey on textual document classification. With the development of st...