Although publicly accessible databases containing speech documents. It requires a great deal of time and effort required to keep them up to date is often burdensome. In an effort to help identify speaker of speech if text is available, text-mining tools, from the machine learning discipline, it can be applied to help in this process also. Here, we describe and evaluate document classification a...

This research is conducted in order to compare the performance of three known text classification techniques namely, Support Vector Machine (SVM) classifier, Naïve Bayes (NB) classifier, and C4.5 Classifier. Text classification aims to automatically assign the text to a predefined category based on linguistic features, and content. These three techniques are compared using a set of Arabic text ...

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 r...

Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents. Text classification just makes up for these deficiencies. In this paper, we propose a novel summarization system called TCSum, which leverages plentiful text classification data to improve the performance of multi-document summarization. TCSu...

In this paper we propose a new method of classifying text documents. Unlike conventional vector space models, the proposed method preserves the sequence of term occurrence in a document. The term sequence is effectively preserved with the help of a novel datastructure called ‘Status Matrix’. Further the corresponding classification technique has been proposed for efficient classification of tex...

Text mining refers to the process of deriving high-quality information from text. Text processing involves in search and replace in electronic format of text. A number of approaches have been developed to represent and classify text documents. Most of the approach tries to attain good classification performance while taking a document only by words. We propose a concept based methodology instea...

This thesis describes research work undertaken in the fields of text and questionnaire mining. More specifically, the research work is directed at the use of text classification techniques for the purpose of summarising the free text part of questionnaires. In this thesis text summarisation is conceived of as a form of text classification in that the classes assigned to text documents can be vi...

Unlabeled documents vastly outnumber labeled documents in text classification. For this reason, semi-supervised learning is well suited to the task. Representing text as a combination of unigrams and bigrams has not shown consistent improvements compared to using unigrams in supervised text classification. Therefore, a natural question is whether this finding extends to semi-supervised learning...

A central problem in information retrieval is the automated classification of text documents. While many existing methods achieve good levels of performance, they generally require levels of computation that prevent them from making sufficiently fast decisions in some applied setting. Using insights gained from examining the way humans make fast decisions when classifying text documents, two ne...