Approach to Hypertext Categorization

The Web is a tremendous source of information, so tremendous that it becomes difficult for human beings to select meaningful information without support. Categorization of documents refers to the problem of automatic classification of a set of documents in classes (or categories or topics). Automatic Text Categorization is an important issue in the text mining. The task is to automatically classify text documents into predefined classes based on their content. Automatic categorization of text documents has become an important research issue now a days. Proper categorization of text documents requires information retrieval, machine learning and Natural language processing (NLP) techniques. Our aim is to focus on important approaches to automatic text categorization based on machine learning technique. Several methods have been proposed for the text documents categorization. We will adapt and create machine learning algorithms for use with the Web's distinctive structures: large-scale, noisy, varied data with potentially rich, human-oriented features using svm.

Language Resources and Evaluation, 2012

Web 2.0 provides user-friendly tools that allow persons to create and publish content online. User generated content often takes the form of short texts (e.g., blog posts, news feeds, snippets, etc). This has motivated an increasing interest on the analysis of short texts and, specifically, on their categorisation. Text categorisation is the task of classifying documents into a certain number of predefined categories. Traditional text classification techniques are mainly based on word frequency statistical analysis and have been proved inadequate for the classification of short texts where word occurrence is too small. On the other hand, the classic approach to text categorization is based on a learning process that requires a large number of labeled training texts to achieve an accurate performance. However labeled documents might not be available, when unlabeled documents can be easily collected. This paper presents an approach to text categorisation which does not need a preclassified set of training documents. The proposed method only requires the category names as user input. Each one of these categories is defined by means of an ontology of terms modelled by a set of what we call proximity equations. Hence, our method is not category occurrence frequency based, but highly depends on the definition of that category and how the text fits that definition. Therefore, the proposed approach is an appropriate method for short text classification where the frequency of occurrence of a category is very small or even zero. Another feature of our method is that the classification process is based on the ability of an extension of the standard Prolog language, named Bousi∼Prolog, for flexible matching and knowledge representation. This declarative approach provides a text classifier which is quick and easy to build, and a classification process which is easy for the user to understand. The results of experiments showed that the proposed method achieved a reasonably useful performance.

Text classification is the process of assigning text documents based on certain categories. A classifier is used to define the appropriate class for each text document based on the input algorithm used for classification. Due to the emerging trends in the field of internet and computers ,billions of text data are processed at a given time and so there is a need for organizing these data to provide easy storage and accessing .Many text classification approaches were developed for effectively solving the problem of identifying and classifying these data .In this project a new text document classifier is proposed by integrating the nearest neighbor classification approach with the support vector machine(SVM) training algorithm. The proposed SVM-NN approach aims to reduce the impact of parameters in classification accuracy. In the training stage, the SVM is utilized to reduce the training samples for each of the available categories to their support vectors (SVs).The SVs from different categories are used as the training data of nearest neighbor classification algorithm in which the nearest centroid distance function is used to calculate the average distance instead of Euclidean function, which reduce time consumption.

Information Retrieval, 2009

Most current methods for automatic text categorization are based on supervised learning techniques and, therefore, they face the problem of requiring a great number of training instances to construct an accurate classifier. In order to tackle this problem, this paper proposes a new semi-supervised method for text categorization, which considers the automatic extraction of unlabeled examples from the Web and

Journal of Systems Science and Systems Engineering, 2007

Text mining, also known as discovering knowledge from the text, which has emerged as a possible solution for the current information explosion, refers to the process of extracting non-trivial and useful patterns from unstructured text. Among the general tasks of text mining such as text clustering, summarization, etc, text classification is a subtask of intelligent information processing, which employs unsupervised learning to construct a classifier from training text by which to predict the class of unlabeled text. Because of its simplicity and objectivity in performance evaluation, text classification was usually used as a standard tool to determine the advantage or weakness of a text processing method, such as text representation, text feature selection, etc. In this paper, text classification is carried out to classify the Web documents collected from XSSC Website (). The performance of support vector machine (SVM) and back propagation neural network (BPNN) is compared on this task. Specifically, binary text classification and multi-class text classification were conducted on the XSSC documents. Moreover, the classification results of both methods are combined to improve the accuracy of classification. An experiment is conducted to show that BPNN can compete with SVM in binary text classification; but for multi-class text classification, SVM performs much better. Furthermore, the classification is improved in both binary and multi-class with the combined method.

36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the

Text categorization is the process of sorting text documents into one or more predefined categories or classes of similar documents. Differences in the results of such categorization arise from the feature set chosen to base the association of a given document with a given category. Advocates of text categorization recognize that the sorting of text documents into categories of like documents reduces the overhead required for fast retrieval of such documents and provides smaller domains in which the users may explore similar documents. In this paper we are interested in examining whether automatic classification of news texts can be improved by a prefiltering the vocabulary to reduce the feature set used in the computations. First we compare artificial neural network and support vector machine algorithms for use as text classifiers of news items. Secondly, we identify a reduction in feature set that provides improved results.

2002

Knowledge Based Systems, 2016

Vector Space Models (VSM) are commonly used in language processing to represent certain aspects of natural language semantics. Semantics of VSM comes from the distributional hypothesis, which states that words that occur in similar contexts usually have similar meanings. In our previous work, we proposed novel semantic smoothing kernels based on classspecific transformations. These kernels use classterm matrices, which can be considered as a new type of VSM. By using the class as the context, these methods can extract class specific semantics by making use of word distributions both in documents and in different classes. In this study, we adapt two of these semantic classification approaches to build a novel and high performance semi-supervised text classification algorithm. These approaches include Helmholtz principle based calculation of term meanings in the context of classes for initial classification and a supervised term weighting based semantic kernel with Support Vector Machines (SVM) for the final classification model. The approach used in the first phase is especially good at learning with very small datasets, while the approach in the second phase is specifically good at eliminating noise in a relatively large and noisy training sets when building a classification model. Overall, as a semantic semi-supervised learning algorithm, our approach can effectively utilize abundant source of unlabeled instances to improve the classification accuracy significantly especially when the amount of labeled instances are limited.

2002

Information Processing & Management, 2004

Most document classification systems consider only the distribution of content words of the documents, ignoring the syntactic information underlying the documents though it is also an important factor. In this paper, we present an approach for classifying large scale unstructured documents by incorporating both the lexical and the syntactic information of documents. For this purpose, we use the co-training algorithm, a partially supervised learning algorithm, in which two separated views for the training data are employed and the small number of labeled data are augmented by the large number of unlabeled data. Since both the lexical and the syntactic information can play roles of separated views for the unstructured documents, the co-training algorithm enhances the performance of document classification using both of them and a large number of unlabeled documents. The experimental results on Reuters-21578 corpus and TREC-7 filtering documents show the effectiveness of unlabeled documents and the use of both the lexical and the syntactic information.