Cross-partition clustering: revealing corresponding themes across related datasets

This article studies the task of discovering correspondences across related domains based on real-world data collections. We address this task through a designated extension of distributional data-clustering methods. The method is empirically demonstrated on synthetic data as well as on texts addres...

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Published inJournal of experimental & theoretical artificial intelligence Vol. 23; no. 2; pp. 153 - 180
Main Authors Marx, Zvika, Dagan, Ido, Shamir, Eli
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis Group 01.06.2011
Taylor & Francis Ltd
Subjects
analogy
Artificial intelligence
Cluster analysis
Collection
Commonality
data clustering
Empirical analysis
Expert systems
information theory
natural language processing
Objectives
Religion
structure mapping
Tasks
text mining
Texts
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Abstract This article studies the task of discovering correspondences across related domains based on real-world data collections. We address this task through a designated extension of distributional data-clustering methods. The method is empirically demonstrated on synthetic data as well as on texts addressing different religions, where the goal is to identify commonalities shared by all religions. This article generalises and demonstrates the empirical improvement relative to our previous studies on this subject, as well as to other comparable methods.
AbstractList This article studies the task of discovering correspondences across related domains based on real-world data collections. We address this task through a designated extension of distributional data-clustering methods. The method is empirically demonstrated on synthetic data as well as on texts addressing different religions, where the goal is to identify commonalities shared by all religions. This article generalises and demonstrates the empirical improvement relative to our previous studies on this subject, as well as to other comparable methods.
This article studies the task of discovering correspondences across related domains based on real-world data collections. We address this task through a designated extension of distributional data-clustering methods. The method is empirically demonstrated on synthetic data as well as on texts addressing different religions, where the goal is to identify commonalities shared by all religions. This article generalises and demonstrates the empirical improvement relative to our previous studies on this subject, as well as to other comparable methods. [PUBLICATION ABSTRACT]
Author Shamir, Eli
Marx, Zvika
Dagan, Ido
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SubjectTerms analogy
Artificial intelligence
Cluster analysis
Collection
Commonality
data clustering
Empirical analysis
Expert systems
information theory
natural language processing
Objectives
Religion
structure mapping
Tasks
text mining
Texts
Title Cross-partition clustering: revealing corresponding themes across related datasets
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