Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is challenging problem especially for the tail because there are only few training documents to build classifier. This paper motivated better explore semantic relationship between each and extreme by taking advantage of both content correlation. Our obj...

In most approaches to text classification, the basic units (terms) used to represent a document are: words (with or without stemming), n-gram characters, phonemes, syllables, multi-words, etc. However, these units are always used exclusively. In this paper, a novel approach is presented that combines two types of such units to represent a document for text classification. Our experiments show t...

Text Document classification aims in associating one or more predefined categories based on the likelihood suggested by the training set of labeled documents. Many machine learning algorithms play a vital role in training the system with predefined categories among which Naïve Bayes has some intriguing facts that it is simple, easy to implement and draws better accuracy in large datasets in spi...

This work proposes a text classification using modified approach of Multinomial Naïve Bayes for justifying and identifying the documents into a particular category. Due to the exploration of the textual information from the electronic digital documents as well as World Wide Web. Naïve Bayes theorem is effective for classification of text documents into the predefined categories by means of the ...

This paper presents an eficient technique for document page layout structure extraction and classification by analyzing the spatial configuration of the bounding boxes of different entities on the given image. The algorithm segments an image into a list of homogeneous zones.The classification algorithm labels each zone as text, table, line-drawing, halftone, ruling, or noise. The text-lines and...