Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
2010-10
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
Authors
Published Date
2010-10
Publisher
University of Minnesota Supercomputing Institute
Type
Article
Abstract
Measuring the similarity of short written contexts is a fundamental problem in Natural Language Processing. This article provides a unifying framework by which short context problems can be categorized both by their intended application and proposed solution. The goal is to show that various problems and methodologies that appear quite different on the surface are in fact very closely related. The axes by which these categorizations are made include the format of the contexts (headed versus headless), the way in which the contexts are to be measured (first-order versus second-order similarity), and the information used to represent the features in the contexts (micro versus macro views). The unifying thread that binds together many short context applications and methods is the fact that similarity decisions must be made between contexts that share few (if any) words in common.
Description
Related to
Replaces
License
Collections
Funding information
University of Minnesota Supercomputing Institute
Isbn identifier
Doi identifier
Previously Published Citation
University of Minnesota Supercomputing Institute Research Report UMSI 2010/118, October 2010
Suggested citation
Pedersen, Ted. (2010). Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/151596.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.