This paper exposes the results of our participation in the Document Mining track at INEX’07. We have focused on the task of classification of XML documents. Our approach to deal with structured document representations uses classification methods for plain text, applied to flattened versions of the documents, where some of their structural properties have been translated to plain text. We have ...

Keywords can be used as attributes for mining rules or a basis measuring the similarity of new (unclassified) documents with existing (classified) ones. The focus is on problem extracting keywords from document collection in order to use them classification. Document classification hot topic machine learning. Typical approaches extract “features,” generally words, document, and feature vectors ...

Text summarization endeavors to produce a summary version of a text, while maintaining the original ideas. The textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such massive amount of data, in order to extract the useful information, is a major undertaking and requires an automatic mechanism to aid with the extant repository of informa...

This paper introduces “Czech Text Document Corpus v 2.0”, a collection of text documents for automatic document classification in Czech language. It is composed of 11,955 text documents provided by the Czech News Agency and is freely available for research purposes at http://home.zcu.cz/ ̃pkral/sw/ . This corpus was created in order to facilitate a straightforward comparison of the document clas...

The main objective is to propose a text classification based on the features selection and preprocessing thereby reducing the dimensionality of the Feature vector and increase the classification accuracy. Text classification is the process of assigning a document to one or more target categories, based on its contents. In the proposed method, machine learning methods for text classification is ...

This paper presents a new method of functional classification of text blocks on a document. It is based on texture analysis and unsupervised classification. Texture is used here to define different classes of text blocks in the document and to direct a possible way of exploration from the most eye-catching data to the less significant text block. The typographical properties of blocks are chara...

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text cla...