research-article

Short-Text Clustering using Statistical Semantics

Authors:

Sepideh Seifzadeh,

Ahmed K. Farahat,

Mohamed S. Kamel,

Fakhri KarrayAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 805 - 810

https://doi.org/10.1145/2740908.2742474

Published: 18 May 2015 Publication History

Abstract

Short documents are typically represented by very sparse vectors, in the space of terms. In this case, traditional techniques for calculating text similarity results in measures which are very close to zero, since documents even the very similar ones have a very few or mostly no terms in common. In order to alleviate this limitation, the representation of short-text segments should be enriched by incorporating information about correlation between terms. In other words, if two short segments do not have any common words, but terms from the first segment appear frequently with terms from the second segment in other documents, this means that these segments are semantically related, and their similarity measure should be high. Towards achieving this goal, we employ a method for enhancing document clustering using statistical semantics. However, the problem of high computation time arises when calculating correlation between all terms. In this work, we propose the selection of a few terms, and using these terms with the Nystr\"om method to approximate the term-term correlation matrix. The selection of the terms for the Nystr\"om method is performed by randomly sampling terms with probabilities proportional to the lengths of their vectors in the document space. This allows more important terms to have more influence on the approximation of the term-term correlation matrix and accordingly achieves better accuracy.

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Li XGuan YFu BLuo Z(2024)Anomaly-aware symmetric non-negative matrix factorization for short text clusteringKnowledge and Information Systems10.1007/s10115-024-02226-z67:2(1481-1506)Online publication date: 4-Nov-2024
https://doi.org/10.1007/s10115-024-02226-z
Yang BGuo HLiu H(2023)Evaluation and assessment of machine learning based user story grouping: A framework and empirical studiesScience of Computer Programming10.1016/j.scico.2023.102943(102943)Online publication date: Mar-2023
https://doi.org/10.1016/j.scico.2023.102943
Yang BMa XWang CGuo HLiu HJin Z(2023)User story clustering in agile development: a framework and an empirical studyFrontiers of Computer Science10.1007/s11704-022-8262-917:6Online publication date: 21-Jan-2023
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Index Terms

Short-Text Clustering using Statistical Semantics
1. Applied computing
  1. Document management and text processing
    1. Document preparation
2. Computing methodologies
  1. Machine learning

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Information

Published In

cover image ACM Other conferences

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Copyright © 2015 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

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Funding Sources

Qatar National Research Fund through National Priority Research Program (NPRP)

Conference

WWW '15

Sponsor:

IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Li XGuan YFu BLuo Z(2024)Anomaly-aware symmetric non-negative matrix factorization for short text clusteringKnowledge and Information Systems10.1007/s10115-024-02226-z67:2(1481-1506)Online publication date: 4-Nov-2024
https://doi.org/10.1007/s10115-024-02226-z
Yang BGuo HLiu H(2023)Evaluation and assessment of machine learning based user story grouping: A framework and empirical studiesScience of Computer Programming10.1016/j.scico.2023.102943(102943)Online publication date: Mar-2023
https://doi.org/10.1016/j.scico.2023.102943
Yang BMa XWang CGuo HLiu HJin Z(2023)User story clustering in agile development: a framework and an empirical studyFrontiers of Computer Science10.1007/s11704-022-8262-917:6Online publication date: 21-Jan-2023
https://doi.org/10.1007/s11704-022-8262-9
Vadapally ADehbozorgi NChowdary Attota D(2022)Minute-Paper Dashboard: Identification of Learner’s Misconceptions Using Topic Modeling on Formative Reflections2022 IEEE Frontiers in Education Conference (FIE)10.1109/FIE56618.2022.9962598(1-5)Online publication date: 8-Oct-2022
https://doi.org/10.1109/FIE56618.2022.9962598
Hendry DDarari FNurfadillah RKhanna GSun MCondylis PTaufik N(2021)Topic Modeling for Customer Service Chats2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)10.1109/ICACSIS53237.2021.9631322(1-6)Online publication date: 23-Oct-2021
https://doi.org/10.1109/ICACSIS53237.2021.9631322
Shen QQiu Y(2021)A Novel Text Ensemble Clustering Based on Weighted Entropy Filtering ModelJournal of Physics: Conference Series10.1088/1742-6596/2024/1/0120452024:1(012045)Online publication date: 1-Sep-2021
https://doi.org/10.1088/1742-6596/2024/1/012045
Akritidis LAlamaniotis MFevgas ABozanis P(2020)Confronting Sparseness and High Dimensionality in Short Text Clustering via Feature Vector Projections2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00129(813-820)Online publication date: Nov-2020
https://doi.org/10.1109/ICTAI50040.2020.00129
Tian GZhao SWang JZhao ZLiu JGuo L(2019)Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDAIEEE Access10.1109/ACCESS.2019.29265597(88231-88242)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2926559
Seifollahi SPiccardi MBorzeshi EKruger B(2019)Taxonomy-Augmented Features for Document ClusteringData Mining10.1007/978-981-13-6661-1_19(241-252)Online publication date: 16-Feb-2019
https://doi.org/10.1007/978-981-13-6661-1_19
Jaffali SJamoussi SKhelifi NHamadou A(2019)Survey on Social Networks Data AnalysisInnovations for Community Services10.1007/978-3-030-37484-6_6(100-119)Online publication date: 15-Dec-2019
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