We propose an unsupervised feature generation algorithm using the repositories of human knowledge for effective text categorization. Conventional bag of words (BOW) depends on the presence / absence of keywords to classify the documents. To understand the actual context behind these keywords, we use knowledge concepts / hyperlinks from external knowledge sources through content and structure mi...

The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to E...

In many multilingual text classification problems, the documents in different languages often share the same set of categories. To reduce the labeling cost of training a classification model for each individual language, it is important to transfer the label knowledge gained from one language to another language by conducting cross language classification. In this paper we develop a novel subsp...

Text classification approach gaining more importance because of the accessibility of large number of electronic documents from a variety of resource. Text categorization (Also called Text Categorization) is the task of assigning predefined categories to documents. It is the method of finding interesting regularities in large textual, where interesting means non trivial, hidden, previously unkno...

today, with rapid growth of the world wide web and creation of internet sites and online text resources, text summarization issue is highly attended by various researchers. extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. when, we are facing into large data volume documents, the extr...

In this paper we present an unsupervised text classification method based on the use of a self organizing map (SOM). A corpus of roughly 200 plain text documents have been considered. Some Scilab scripts have been prepared to read and process these documents, train the neural network and graphically render the

In this paper we propose a new method of classifying text documents. Unlike conventional vector space models, the proposed method preserves the sequence of term occurrence in a document. The term sequence is effectively preserved with the help of a novel datastructure called ‘Status Matrix’. Further the corresponding classification technique has been proposed for efficient classification of tex...

Text classification is the problem of assigning pre-defined class labels to incoming, unclassified documents. The class labels are defined based on a set of examples of pre-classified documents, used as a training corpus. For text classification, a number of approaches have been proposed such as Support Vector machines, Decision trees, k-nearest-neighbor classification, Linear Least Square fit ...

Text classification has traditionally been one of the most popular problems in information retreival, natural language processing and machine learning. In the simplest case, the task of text classification [1] is as follows: A set of training documents T = {X1, X2, ...Xm} , each labelled with a class value from a set of k distinct labels, from the set {1, 2, ..k}, is used to learn a classificat...