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University of Illinois at Urbana-Champaign
Improve KL -Divergence Language Models in Information Retrieval Using Corpus Local Structures
Abstract
dc:descriptionIn summary, this thesis studies KL-divergence from different perspectives and proposes several new models to address the existing problems in KL-divergence language models. It results in more effective retrieval models, which should potentially benefit all retrieval applications.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Tao, Tao
- Contributors dc:contributor
-
- Zhai, ChengXiang
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3301233
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/81805