International Journal of Web Information Systems

The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have made this classification problem very difficult. We investigate four different methods for document classification: the naive Bayes classifier, the nearest neighbour classifier, decision trees and a subspace method. These were applied to seven-class Yahoo news groups (business, entertainment, health, international, politics, sports and technology) individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classifiers on our data sets. Combinations of multiple classifiers did not always improve the classification accuracy compared to the best individual classifier. Among the three different combination approaches, our adaptive classifier combination method introduced here performed the best. The best classification accuracy that we are able to achieve on this seven-class problem is approximately 83%, which is comparable to the performance of other similar studies. However, the classification problem considered here is more difficult because the pattern classes used in our experiments have a large overlap of words in their corresponding documents.

Information Systems, 2008

Text categorization is the task of assigning predefined categories to natural language text. With the widely used "bag-ofword" representation, previous researches usually assign a word with values that express whether this word appears in the document concerned or how frequently this word appears. Although these values are useful for text categorization, they have not fully expressed the abundant information contained in the document. This paper explores the effect of other types of values, which express the distribution of a word in the document. These novel values assigned to a word are called distributional features, which include the compactness of the appearances of the word and the position of the first appearance of the word. The proposed distributional features are exploited by a tfidf style equation, and different features are combined using ensemble learning techniques. Experiments show that the distributional features are useful for text categorization. In contrast to using the traditional term frequency values solely, including the distributional features requires only a little additional cost, while the categorization performance can be significantly improved. Further analysis shows that the distributional features are especially useful when documents are long and the writing style is casual.

Webology, 2015

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information, popularly known as the digital/electronic information is in the form of documents, conference material, publications, journals, editorials, web pages, e-mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. In this paper we have studied the existing work in the area of text classification which will allow us to have a fair evaluation of the progress made in this field till date. We have investigated the papers to the ...

Lecture Notes in Computer Science, 2009

Automatic categorization of text documents has become an important area of research in the last two decades, with features that make it significantly more difficult than the traditional classification tasks studied in machine learning. A more recent development is the need to classify hypertext documents, most notably web pages. These have features that add further complexity to the categorization task but also offer the possibility of using information that is not available in standard text classification, such as metadata and the content of the web pages that point to and are pointed at by a web page of interest. This chapter surveys the state of the art in text categorization and hypertext categorization, focussing particularly on issues of representation that differentiate them from 'conventional' classification tasks and from each other.

2007

Studies in Computational Intelligence, 2009

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

International Journal of Advanced Networking and Applications, 2020

There are huge data from unstructured text obtained daily from various resources like emails, tweets, social media posts, customer comments, reviews, and reports in many different fields, etc. Unstructured text data can be analyzed to obtain useful information that will be used according to the purpose of the analysis also the domain that the data was obtained from it. Because of the huge amount of the data the human manually analysis of these texts is not possible, so we have to automatic analysis. The topic analysis is the Natural Language Processing (NLP) technology that organizes and understands large collections of text data, by identifying the topics, finding patterns and semantic. There two common approaches for topic analysis, topic modeling, and topic classification each approach has different algorithms to apply that will be discussed.

2008

TEXT CATEGORIZATION OF COMMERCIAL WEB PAGES E. Binaghi, M. Carullo, I. Gallo and M. Madaio Universit´a degli Studi dell'Insubria Via Mazzini 5, 21100 Varese, Italy email: [email protected] ABSTRACT In this paper we describe a new on-line document categorization strategy that can be integrated within Web applications. A salient aspect is the use of neural learning in both representation and classification tasks.

International Journal on Natural Language Computing, 2016

In this paper firstly I have compared Single Label Text Categorization with Multi Label Text Categorization in detail then I have compared Document Pivoted Categorization with Category Pivoted Categorization in detail. For this purpose I have given the general definition of Text Categorization with its mathematical notation for the purpose of its frugality and cost effectiveness. Then with the help of mathematical notation and set theory ,I have converted the general definitions of

IEEE Transactions on Neural Networks, 1999

AbstractÐWe develop an automatic text categorization approach and investigate its application to text retrieval. The categorization approach is derived from a combination of a learning paradigm known as instance-based learning and an advanced document retrieval technique known as retrieval feedback. We demonstrate the effectiveness of our categorization approach using two realworld document collections from the MEDLINE database. Next, we investigate the application of automatic categorization to text retrieval. Our experiments clearly indicate that automatic categorization improves the retrieval performance compared with no categorization. We also demonstrate that the retrieval performance using automatic categorization achieves the same retrieval quality as the performance using manual categorization. Furthermore, detailed analysis of the retrieval performance on each individual test query is provided.

IEEE Transactions on Knowledge and Data Engineering, 1999

AbstractÐWe develop an automatic text categorization approach and investigate its application to text retrieval. The categorization approach is derived from a combination of a learning paradigm known as instance-based learning and an advanced document retrieval technique known as retrieval feedback. We demonstrate the effectiveness of our categorization approach using two realworld document collections from the MEDLINE database. Next, we investigate the application of automatic categorization to text retrieval. Our experiments clearly indicate that automatic categorization improves the retrieval performance compared with no categorization. We also demonstrate that the retrieval performance using automatic categorization achieves the same retrieval quality as the performance using manual categorization. Furthermore, detailed analysis of the retrieval performance on each individual test query is provided.