research-article

Improving document clustering using automated machine translation

Authors:

Ian DavidsonAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 645 - 653

https://doi.org/10.1145/2396761.2396844

Published: 29 October 2012 Publication History

Abstract

With the development of statistical machine translation, we have ready-to-use tools that can translate documents from one language to many other languages. These translations provide different yet correlated views of the same set of documents. This gives rise to an intriguing question: can we use the extra information to achieve a better clustering of the documents? Some recent work on multiview clustering provided positive answers to this question. In this work, we propose an alternative approach to address this problem using the constrained clustering framework. Unlike traditional Must-Link and Cannot-Link constraints, the constraints generated from machine translation are dense yet noisy. We show how to incorporate this type of constraints by presenting two algorithms, one parametric and one non-parametric. Our algorithms are easy to implement, efficient, and can consistently improve the clustering of real data, namely the Reuters RCV1/RCV2 Multilingual Dataset. In contrast to existing multiview clustering algorithms, our technique does not need the compatibility or the conditional independence assumption, nor does it involve subtle parameter tuning.

References

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Reuters RCV1/RCV2 multilingual dataset: http://multilingreuters.iit.nrc.ca/ReutersMultiLingualMultiView.htm

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Cited By

Zhang KQian BZhang XZheng Q(2024)Using cross-domain knowledge augmentation to explore comorbidity in electronic health records dataExpert Systems with Applications10.1016/j.eswa.2024.125644(125644)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125644
Hao ZXu SZhong GLiu B(2020)Pairwise-Constraints Based Semi-Supervised Fuzzy Clustering with Entropy Regularization2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)10.1109/AEMCSE50948.2020.00036(137-144)Online publication date: Apr-2020
https://doi.org/10.1109/AEMCSE50948.2020.00036
Zhi WQian BDavidson I(2017)Scalable Constrained Spectral Clustering via the Randomized Projected Power Method2017 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2017.162(1201-1206)Online publication date: Nov-2017
https://doi.org/10.1109/ICDM.2017.162
Show More Cited By

Index Terms

Improving document clustering using automated machine translation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Zhang KQian BZhang XZheng Q(2024)Using cross-domain knowledge augmentation to explore comorbidity in electronic health records dataExpert Systems with Applications10.1016/j.eswa.2024.125644(125644)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125644
Hao ZXu SZhong GLiu B(2020)Pairwise-Constraints Based Semi-Supervised Fuzzy Clustering with Entropy Regularization2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)10.1109/AEMCSE50948.2020.00036(137-144)Online publication date: Apr-2020
https://doi.org/10.1109/AEMCSE50948.2020.00036
Zhi WQian BDavidson I(2017)Scalable Constrained Spectral Clustering via the Randomized Projected Power Method2017 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2017.162(1201-1206)Online publication date: Nov-2017
https://doi.org/10.1109/ICDM.2017.162
Wang CLai JYu P(2016)Multi-View Clustering Based on Belief PropagationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.250374328:4(1007-1021)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1109/TKDE.2015.2503743
Jiameng Pu Zhang QZhang LDu BJane You (2016)Multiview clustering based on Robust and Regularized Matrix Approximation2016 23rd International Conference on Pattern Recognition (ICPR)10.1109/ICPR.2016.7900020(2550-2555)Online publication date: Dec-2016
https://doi.org/10.1109/ICPR.2016.7900020
Xu YWang CLai J(2016)Weighted Multi-view Clustering with Feature SelectionPattern Recognition10.1016/j.patcog.2015.12.00753:C(25-35)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.patcog.2015.12.007
Qian BWang XYe JDavidson I(2015)A Reconstruction Error Based Framework for Multi-Label and Multi-View LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2014.233986027:3(594-607)Online publication date: 1-Mar-2015
https://doi.org/10.1109/TKDE.2014.2339860
Ye CWen WWang C(2015)Chinese-Tibetan bilingual clustering based on random walkNeurocomputing10.1016/j.neucom.2015.02.003158:C(32-41)Online publication date: 22-Jun-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.02.003
Tran KChristen PHe QIyengar ANejdl WPei JRastogi R(2013)Identifying multilingual Wikipedia articles based on cross language similarity and activityProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507825(1485-1488)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2507825

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