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Web is gigantic and being constantly update. Bangla news in web are rapidly grown in the era of information age where each news site has its own different layout and categorization for grouping news. These heterogeneity of layout and categorization can not always satisfy individual user's need. Removing these heterogeneity and classifying the news articles according to user preference is a formidable...
Labeled datasets are essential for text categorization. They are used to train a classifier, or as a benchmark collection to evaluate categorization algorithms. However, labeling a large-scale document set is extremely expensive because it involves much human labour, and the labeling process itself is subjective rather than objective. Therefore, labels assigned to documents by only one human editor...
In this paper, we propose a framework to answer questions of opinion type. The data source is the web pages returned from the search engine. By using Bayes Classifier, the main texts on the pages are classified into three categories at sentence level: positive review, negative review and neutral review. K-means method is used to cluster the sentences of positive review and negative review respectively...
In this work we look into analyzing blogs to classify products according to users' query. Blogs can be found over the Internet where buyers share their opinions on different products that are available in the market. Such pages may prove to be good guides for a prospective buyer. However, going through a large number of blogs and to convert their opinions into a meaningful decision is often difficult...
This paper proposes an Internet environment called BrailleMUSE to translate digital music scores into Braille scores. In experiments with expert translators, a translation error occurred only at extremely slight frequency. Moreover, the experts reported that working hours to produce Braille scores were shortened as the drafts the BrailleMUSE provided were only modified. Also, the BrailleMUSE has a...
Style-based text authorship identification extracts features from authorship-known texts, constructs classifier and then identifies disputed texts. Authorship identification belongs to the domain of style classification and is a branch of text classification. In contrast with text classification which deals with the content of texts, authorship identification focuses on the form property of texts...
Sem@ntica is a system for extracting the information contained in collections of documents into a knowledge base. It combines high quality conventional named entity analysis with an ontology class labeling capability for open class words. The ontology comprises an upper ontology and one or more domain ontologies. The system has tools for rapidly designing the ontology and mapping segments of Word...
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