poster

PTM: probabilistic topic mapping model for mining parallel document collections

Authors:

ChengXiang Zhai,

Nikos AnerousisAuthors Info & Claims

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

Pages 1653 - 1656

https://doi.org/10.1145/1871437.1871696

Published: 26 October 2010 Publication History

Abstract

Many applications generate a large volume of parallel document collections. A parallel document collection consists of two sets of documents where the documents in each set correspond to each other and form semantic pairs (e.g., pairs of problem and solution descriptions in a help-desk setting). Although much work has been done on text mining, little previous work has attempted to mine such a novel kind of text data. In this paper, we propose a new probabilistic topic model, called Probabilistic Topic Mapping (PTM) model, to mine parallel document collections to simultaneously discover latent topics in both sets of documents as well as the mapping of topics in one set to those in the other. We evaluate the PTM model on one real parallel document collection in IT service domain. We show that PTM can effectively discover meaningful topics, as well as their mappings, and it's also useful for improving text matching and retrieval when there's a vocabulary gap.

References

[1]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.

[2]

G. Cong, L.Wang, C.-Y. Lin, Y.-I. Song, and Y. Sun. Finding question-answer pairs from online forums. In SIGIR '08, pages 467--474, New York, NY, USA, 2008. ACM.

Digital Library

[3]

A. Corrada-Emmanuel and W. B. Croft. Answer models for question answering passage retrieval. In SIGIR '04, pages 516--517, New York, NY, USA, 2004. ACM.

Digital Library

[4]

H. T. Dang, D. Kelly, and J. Lin. Overview of the trec 2007 question answering track. In Proceeding of the 16th Text Retrieval Conference, 2007.

[5]

T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn., 42(1-2):177--196, 2001.

Digital Library

[6]

J. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieval. In SIGIR '01, pages 111--119, New York, USA, 2001. ACM.

Digital Library

[7]

D. Mimno, H. M. Wallach, J. Naradowsky, D. A. Smith, and A. Mccallum. Polylingual topic models. In EMNLP'09, pages 880--889, Singapore, August 2009. Association for Computational Linguistics.

Digital Library

[8]

R. M. Nallapati, A. Ahmed, E. P. Xing, and W. W. Cohen. Joint latent topic models for text and citations. In KDD '08, pages 542--550, New York, USA, 2008. ACM.

Digital Library

[9]

D. Ramage, P. Heymann, C. D. Manning, and H. Garcia-Molina. Clustering the tagged web. In WSDM '09, pages 54--63, New York, USA, 2009. ACM.

Digital Library

[10]

L. Shrestha and K. McKeown. Detection of question-answer pairs in email conversations. In COLING '04, Morristown, NJ, USA, 2004. Association for Computational Linguistics.

Digital Library

[11]

R. Soricut and E. Brill. Automatic question answering using the web: Beyond the factoid. Inf. Retr., 9(2):191--206, 2006.

Digital Library

[12]

M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. Griffiths. Probabilistic author-topic models for information discovery. In KDD '04, pages 306--315, New York, USA, 2004. ACM.

Digital Library

[13]

X. Xue, J. Jeon, and W. B. Croft. Retrieval models for question and answer archives. In SIGIR '08, pages 475--482, New York, NY, USA, 2008. ACM.

Digital Library

[14]

C. Zhai and J. Lafferty. Model-based feedback in the language modeling approach to information retrieval. In CIKM '01, pages 403--410, New York, USA, 2001. ACM.

Digital Library

[15]

D. Zhou, J. Bian, S. Zheng, H. Zha, and C. L. Giles. Exploring social annotations for information retrieval. In WWW '08, pages 715--724, New York, NY, USA, 2008. ACM.

Digital Library

Cited By

Fei GChen ZMukherjee ALiu B(2018)Discovering Correspondence of Sentiment Words and AspectsComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-75487-1_18(233-245)Online publication date: 21-Mar-2018
https://doi.org/10.1007/978-3-319-75487-1_18
Miao GGuan ZMoser LYan XTao SAnerousis NSun JYang QAgarwal DPei J(2012)Latent association analysis of document pairsProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339752(1415-1423)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2339530.2339752

Index Terms

PTM: probabilistic topic mapping model for mining parallel document collections
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

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.

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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Publication History

Published: 26 October 2010

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CIKM '10: International Conference on Information and Knowledge Management

October 26 - 30, 2010

ON, Toronto, Canada

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

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Cited By

Fei GChen ZMukherjee ALiu B(2018)Discovering Correspondence of Sentiment Words and AspectsComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-75487-1_18(233-245)Online publication date: 21-Mar-2018
https://doi.org/10.1007/978-3-319-75487-1_18
Miao GGuan ZMoser LYan XTao SAnerousis NSun JYang QAgarwal DPei J(2012)Latent association analysis of document pairsProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339752(1415-1423)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2339530.2339752

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