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. / Dissertation/Thesis / Masters Thesis Computer Science 2015
| Identifer | oai:union.ndltd.org:asu.edu/item:29859 |
| Date | January 2015 |
| Contributors | Kedia, Nitesh (Author), Davulcu, Hasan (Advisor), Corman, Steve R (Committee member), Li, Baoxin (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 27 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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