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...

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...

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...

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...

In this paper, a cross-language patent retrieval and classification system is presented to integrate the query translation using various free web translators on the internet and the document classification. The language-independent indexing method was used to process the multilingual patent documents, and the query translation method was used to translate the query from the source language to t...

In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a particular class (called positive class) and a set U of unlabeled documents that contains documents from class P and also other types of documents (called negative class documents), we want to build a classifier to cla...

This paper presents a novel holistic technique for classifying Arabic handwritten text documents. The classification of Arabic handwritten documents is performed in several steps. First, the Arabic handwritten document images are segmented into words, and then each word is segmented into its connected parts. Second, several structural and statistical features are extracted from these connected ...