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A Semantic Triplet Based Story Classifier


Abstract Text classification, in the artificial intelligence domain, is an activity in which text documents are automatically classified into predefined categories using machine learning techniques. An example of this is classifying uncategorized news articles into different predefined categories such as "Business", "Politics", "Education", "Technology" , etc. In this thesis, supervised machine learning approach is followed, in which a module is first trained with pre-classified training data and then class of test data is predicted. Good feature extraction is an important step in the machine learning approach and hence the main component of this text classifier is semantic triplet based features in addition t... (more)
Created Date 2013
Contributor Karad, Ravi Chandravadan (Author) / Davulcu, Hasan (Advisor) / Corman, Steven (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Subject Computer science / A Semantic Triplet Based Story Classifier / Machine learning / Natural Language Processing / Ravi Karad / SVM (Support Vector Machine) classifier / Text classification
Type Masters Thesis
Extent 65 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis