Text documents stored in information systems usually consist of more information than the pure concatenation of words, i.e., they also contain typographic information. Because conventional text retrieval methods evaluate only the word frequency, they miss the information provided by typography, e.g., regarding the importance of certain terms. In order to overcome this weakness, we present an ap...

Automated text classification has been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. In general, text classification plays an important role in information extraction and summarization, text retrieval, and questionanswering. This paper illustrates the text classification process using machine learn...

The Era of digitization induces the need of domainclassification in both the on-line and off-line applications. The necessity of automatic text classification arises for utilizing it in diverse fields. Hence various methodologies like Machine Learningalgorithms were proposed to do the same. Here automatic document classification of Tamil documents have been proposed by considering the exponenti...

Text classification is a process where documents are categorized usually by topic, place, readability easiness, etc. For text classification by topic, a well-known method is Singular Value Decomposition. For text classification by readability, “Flesh Reading Ease index” calculates the readability easiness level of a document (e.g. easy, medium, advanced). In this paper, we propose Singular Valu...

Internet is a pool of information, which contains billions of text documents which are stored in compressed format. In literature we can find many text classification algorithms which work on uncompressed text documents. In this paper, we propose a novel representation scheme for a given text document using compression technique. Further, proposed representation scheme is used to develop a meth...

We study the problem of constructing the topic-based model over different domains for text classification. In real-world applications, there are abundant unlabeled documents but sparse labeled documents. It is challenging to construct a reliable and adaptive model to classify a large amount of documents containing different domains. The classifiers trained from a source domain shall perform poo...

Text categorization is the task of assigning text or documents into pre-specified classes or categories. For an improved classification of documents text-based learning needs to understand the context, like humans can decide the relevance of a text through the context associated with it, thus it is required to incorporate the context information with the text in machine learning for better clas...

Text document is multifaceted object and associated with many properties such as multi labeledness. Under this a single text document can inherently belongs to more than one category simultaneously. Traditional single label and multi class text class ification paradigms cannot efficiently classify such multifaceted text corpus. Through our paper we are proposing a graph based frame work for Mul...

1. Profiling and classification of scientific documents with SAS Text Miner SAS Institute (www.sas.com) and the European Molecular Biology Laboratory (EMBL)/ the ELM Consortium (http://elm.eu.org) are cooperating on the development of a text mining-application for the automated identification and ranking of scientific articles. The so-called “topic scoring engine” is based on the SAS Text Miner...