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...

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

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...

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 ...

A text classification method using Kohonen’s Self Organizing Network is presented here. The proposed method can classify a set of text documents into a number of classes depending on their contents where the number of such classes is not known a priori. Text documents from various faculties of games are considered for experimentation. The method is found to provide satisfactory results for larg...

Categorization of biomedical articles is a central task for supporting various curation efforts. It can also form the basis for effective biomedical text mining. Automatic text classification in the biomedical domain is thus an active research area. Contests organized by the KDD Cup (2002) and the TREC Genomics track (since 2003) defined several annotation tasks that involved document classific...

The task of automatic text summarization consists of generating a summary of the original text that allows the user to obtain the main pieces of information available in that text, but with a much shorter reading time. This is an increasingly important task in the current era of information overload, given the huge amount of text available in documents. In this paper the automatic text summariz...

Spectral Graph Transducer(SGT) is one of the superior graph-based transductive learning methods for classification. As for the Spectral Graph Transducer algorithm, a good graph representation for data to be processed is very important. In this paper, we try to incorporate Latent Semantic Indexing(LSI) into SGT for text classification. Firstly, we exploit LSI to represent documents as vectors in...

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...

Keyphrases are useful for variety of purposes including: text clustering, classification, content-based retrieval, and automatic text summarization. A small amount of documents have author-assigned keyphrases. Manual assignment of the keyphrases to existing documents is a tedious task, therefore, automatic keyphrase extraction has been extensively used to organize documents. Existing automatic ...