Mita K. Dalal

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Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering(More)
Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given text document to a set of pre-defined categories based on the features extracted from its textual content. This paper attempts to automatically classify the textual entries made by bloggers on various sports blogs, to the appropriate category of sport(More)
Automatic Text Summarization is a specialized text mining task of generating a summary or abstract from single or multiple input text documents. Various heuristic and semi-supervised learning methods have been explored by researchers in this field to generate generic as well as user-oriented summaries. This paper examines the effectiveness of well-known(More)
In today's world the volume of information is dramatically increasing, and the value of that information is growing fast. Modern organizations deals with terabytes of text, such as email, that often plays a significant role in their day-today operations. Even small and medium-sized organizations are dealing with growing volumes of text that require rapid(More)
Today's world is relied on computer technology's advancement to get the best whatever they want or select. Since the possibility of sharing and exchanging information on internet, it is really easiest task than ever before and same technology aids are providing us ample amount of data, information while selecting best of services, best of products(More)
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Due to its wide distribution, openness and highly dynamic data, the resources on the web are greatly scattered and they have no unified management and structure. Near about 90 % web data is unstructured and needed to be(More)
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