research-article

Collaborative Dual-PLSA: mining distinction and commonality across multiple domains for text classification

Authors:

Hui XiongAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 359 - 368

https://doi.org/10.1145/1871437.1871486

Published: 26 October 2010 Publication History

Abstract

The distribution difference among multiple data domains has been considered for the cross-domain text classification problem. In this study, we show two new observations along this line. First, the data distribution difference may come from the fact that different domains use different key words to express the same concept. Second, the association between this conceptual feature and the document class may be stable across domains. These two issues are actually the distinction and commonality across data domains.

Inspired by the above observations, we propose a generative statistical model, named Collaborative Dual-PLSA (CD-PLSA), to simultaneously capture both the domain distinction and commonality among multiple domains. Different from Probabilistic Latent Semantic Analysis (PLSA) with only one latent variable, the proposed model has two latent factors y and z, corresponding to word concept and document class respectively. The shared commonality intertwines with the distinctions over multiple domains, and is also used as the bridge for knowledge transformation. We exploit an Expectation Maximization (EM) algorithm to learn this model, and also propose its distributed version to handle the situation where the data domains are geographically separated from each other. Finally, we conduct extensive experiments over hundreds of classification tasks with multiple source domains and multiple target domains to validate the superiority of the proposed CD-PLSA model over existing state-of-the-art methods of supervised and transfer learning. In particular, we show that CD-PLSA is more tolerant of distribution differences.

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Raghunathan NSaravanakumar K(2023)Challenges and Issues in Sentiment Analysis: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2023.329304111(69626-69642)Online publication date: 2023
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Li PJiang ZQue MHu YTuzhilin AZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467140(3172-3180)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467140
Chen CGuo JSong BDiaz FShah CSuel TCastells PJones RSakai T(2021)Dual Attention Transfer in Session-based Recommendation with Multi-dimensional IntegrationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462866(869-878)Online publication date: 11-Jul-2021
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Index Terms

Collaborative Dual-PLSA: mining distinction and commonality across multiple domains for text classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning

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cover image ACM Conferences

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

Copyright © 2010 ACM.

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Published: 26 October 2010

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October 26 - 30, 2010

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https://doi.org/10.1109/ACCESS.2023.3293041
Li PJiang ZQue MHu YTuzhilin AZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467140(3172-3180)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467140
Chen CGuo JSong BDiaz FShah CSuel TCastells PJones RSakai T(2021)Dual Attention Transfer in Session-based Recommendation with Multi-dimensional IntegrationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462866(869-878)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462866
Wang DJing BLu CWu JLiu GDu CZhuang F(2021)Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297922532:2(736-747)Online publication date: Feb-2021
https://doi.org/10.1109/TNNLS.2020.2979225
Li PTuzhilin A(2021)Dual Metric Learning for Effective and Efficient Cross-Domain RecommendationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3074395(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3074395
Li PTuzhilin ACaverlee JHu XLalmas MWang W(2020)DDTCDRProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371793(331-339)Online publication date: 20-Jan-2020
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Chen CShen FXu JYan R(2020)Probabilistic Latent Semantic Analysis-Based Gear Fault Diagnosis Under Variable Working ConditionsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2019.292541069:6(2845-2857)Online publication date: Jun-2020
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Zhuang FQi ZDuan KXi DZhu YZhu HXiong HHe Q(2020)A Comprehensive Survey on Transfer LearningProceedings of the IEEE10.1109/JPROC.2020.3004555(1-34)Online publication date: 2020
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Xue JShen GLi YWang JZafar I(2020)Dynamic Stakeholder-Associated Topic Modeling on Public Concerns in Megainfrastructure Projects: Case of Hong Kong–Zhuhai–Macao BridgeJournal of Management in Engineering10.1061/(ASCE)ME.1943-5479.000084536:6Online publication date: Nov-2020
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Wu MPan SZhu XZhou CPan L(2019)Domain-Adversarial Graph Neural Networks for Text Classification2019 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2019.00075(648-657)Online publication date: Nov-2019
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