Concept aware co-occurrence and its applications

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Publication Type thesis
School or College College of Engineering
Department School of Computing
Author Simonic, Klemen
Title Concept aware co-occurrence and its applications
Date 2015-08
Description Term co-occurrence data has been extensively used in many applications ranging from information retrieval to word sense disambiguation. There are two major limitations of co-occurrence data. The first limitation is known as the data sparseness problem or the zero frequency problem: For a majority of pairs, the probability that they co-occur in even a large corpus is very small. The second limitation is that in co-occurrence data, each term is considered as a meaningless symbol, or in other words, terms do not have types, or any semantic relationships with other terms. In this paper, we introduce a novel approach to address these two limitations. We create concept aware co-occurrence data wherein each term is not a symbol, but an entry in a large-scale, data-driven semantic network. We show that with concepts or types, we are able to address the data sparseness problem through generalization. Furthermore, using concept co-occurrence, we show that our approach can benefit a large range of applications, including short text understanding.
Type Text
Publisher University of Utah
Subject Concept co-occurrence; Isa relations; Query undertstanding; Short text understanding; Term co-occurrence
Dissertation Institution University of Utah
Dissertation Name Master of Science
Language eng
Rights Management Copyright © Klemen Simonic 2015
Format Medium application/pdf
Format Extent 26,863 bytes
Identifier etd3/id/3839
ARK ark:/87278/s6xs93pf
Setname ir_etd
Date Created 2016-06-06
Date Modified 2018-03-22
ID 197390
Reference URL