Full metadata
Title
Story detection using generalized concepts
Description
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.
Date Created
2015
Contributors
- Kedia, Nitesh (Author)
- Davulcu, Hasan (Thesis advisor)
- Corman, Steve R (Committee member)
- Li, Baoxin (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
vii, 19 pages : color illustrations
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.29859
Statement of Responsibility
by Nitesh Kedia
Description Source
Viewed on July 14, 2015
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2015
Note type
thesis
Includes bibliographical references (page 19)
Note type
bibliography
Field of study: Computer science
System Created
- 2015-06-01 08:10:48
System Modified
- 2021-08-30 01:29:09
- 2 years 8 months ago
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