Associating Terms with Text Categories

Classifying Text Documents by Associating Terms With Text Categories

Survey of Machine Learning Techniques in Textual Document Classification

Classification of Text Document points towards associating one or more predefined categories based on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays an important role in training the system with predefined categories. The importance of Machine learning approach has felt because of which the study has been taken up for text document clas...

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Text Document classification aims in associating one or more predefined categories based on the likelihood suggested by the training set of labeled documents. Many machine learning algorithms play a vital role in training the system with predefined categories among which Naïve Bayes has some intriguing facts that it is simple, easy to implement and draws better accuracy in large datasets in spi...

Associating Documents to Concept Maps in Context

To be useful, automatic document classification systems must accurately place documents in categories that are meaningful to users. Because concept mapping externalizes humans’ conceptualizations of a domain, concept maps provide meaningful categories for organizing documents. Since electronic concept-mapping tools provide mechanisms for using concept maps for effective document access, using c...

From Terms to Categories: Testing the Significance of Co-occurrences between Ontological Categories

The co-occurrence of terms in a text corpus may indicate the presence of a relation between the referents of these terms. We expect co-occurrence-based methods to identify association relations that cannot be found using static patterns. We developed a new method to identify associations between ontological categories in text using the co-occurrence of terms that designate these categories. We ...