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Story Detection Using Generalized Concepts


Abstract A major challenge in automated text analysis is that different words are used for related concepts. Analyzing text at the surface level would treat related concepts (i.e. actors, actions, targets, and victims) as different objects, potentially missing common narrative patterns. Generalized concepts are used to overcome this problem. Generalization may result into word sense disambiguation failing to find similarity. This is addressed by taking into account contextual synonyms. Concept discovery based on contextual synonyms reveal information about the semantic roles of the words leading to concepts. Merger engine generalize the concepts so that it can be used as features in learning algorithms.
Created Date 2015
Contributor Kedia, Nitesh (Author) / Davulcu, Hasan (Advisor) / Corman, Steve R (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Subject Computer science / Social sciences education / Generalized Concepts / Hierarchical Merging / Machine Learning / Natural Language Processing / Story Detection / Text Mining
Type Masters Thesis
Extent 27 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis