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Article

On the collective classification of email "speech acts"

Authors:

Vitor R. Carvalho,

William W. CohenAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 345 - 352

https://doi.org/10.1145/1076034.1076094

Published: 15 August 2005 Publication History

Abstract

We consider classification of email messages as to whether or not they contain certain "email acts", such as a request or a commitment. We show that exploiting the sequential correlation among email messages in the same thread can improve email-act classification. More specifically, we describe a new text-classification algorithm based on a dependency-network based collective classification method, in which the local classifiers are maximum entropy models based on words and certain relational features. We show that statistically significant improvements over a bag-of-words baseline classifier can be obtained for some, but not all, email-act classes. Performance improvements obtained by collective classification appears to be consistent across many email acts suggested by prior speech-act theory.

References

[1]

A. Berger, S. Della Pietra and V.A. Della Pietra. A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics, 22(1):39--71, 1996.

Digital Library

[2]

J. Carletta. Assessing Agreement on Classification Tasks: The Kappa Statistic. Computational Linguistics, 22(2):249--254, 1996.

Digital Library

[3]

V.R. Carvalho, W. Wu, W.W. Cohen and J. Kleinberg. Predicting Leadership Roles in Email Workgroups. Work in Progress, http://www.cs.cmu.edu/~vitor/publications.html.

[4]

W.W. Cohen, V.R. Carvalho and T.M. Mitchell. Learning to Classify Email into "Speech Acts". Proceedings of the EMNLP, Barcelona, Spain, July 2004.

[5]

W.W. Cohen. Minorthird: Methods for Identifying Names and Ontological Relations in Text using Heuristics for Inducing Regularities from Data. In http://minorthird.sourceforge.net, 2004.

[6]

S. Chakrabarti and P. Indyk. Enhanced Hypertext Categorization Using Hypelinks. Proceedings of the ACM SIGMOD, Seattle, Washington, 1998.

Digital Library

[7]

S. Geman and D. Geman. Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6):721--741, 1984.

[8]

D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research, (1):49--75, 2000.

Digital Library

[9]

D. Jensen, J. Neville and B. Gallagher. Why Collective Classification Inference Improves Relational Classification. Proceedings of the 10th ACM SIGKDD, 2004.

Digital Library

[10]

R.E. Kraut, S.R. Fussell, F.J. Lerch,and A. Espinosa. A. Coordination in Teams: Evidence from a Simulated Management Game. To appear in the Journal of Organizational Behavior.

[11]

A. Leusky. Email is a Stage: Discovering People Roles from Email Archives. ACM SIGIR, 2004.

Digital Library

[12]

H. Murakoshi, A. Shimazu, and K. Ochimizu. Construction of Deliberation Structure in Email Communication. Pacific Association for Computational Linguistics, 1999.

[13]

J. Neville and D. Jensen. Iterative Classification in Relational Data. AAAI-2000 Workshop on Learning Statistical Models from Relational Data. AAAI Press, 2000.

[14]

J. Neville, D. Jensen, and J. Rattigan. Statistical Relational Learning: Four Claims and a Survey. Workshop on Learning Statistical Models from Relational Data, 18th IJCAI, 2003.

[15]

F. Sha and F. Pereira. Shallow Parsing with Conditional Random Fields. Proceedings of the HLT-NAACL, ACM, 2003.

Digital Library

[16]

M. Schoop. A Language-Action Approach to Electronic Negotiations. Proc. of the Eighth Annual Working Conference on Language-Action Perspective on Communication Modelling, 2003.

[17]

T. Winograd and C.F. Flores Understanding Computers and Cognition. Ablex Publishing Corp., Norwood, NJ, 1986.

Digital Library

Cited By

Žitnik SGordon Smith G(2023)Automated analysis of postings in fourth grade online discussions to help teachers keep students on-trackInteractive Learning Environments10.1080/10494820.2023.220432732:8(4587-4612)Online publication date: 11-May-2023
https://doi.org/10.1080/10494820.2023.2204327
Zhong L(2022)Deep Neural Networks for Text ClassificationProceedings of the 7th International Conference on Cyber Security and Information Engineering10.1145/3558819.3565095(292-296)Online publication date: 23-Sep-2022
https://dl.acm.org/doi/10.1145/3558819.3565095
Shabat HAbbas N(2020)Enhance the Performance of Independent Component Analysis for Text Classification by Using Particle Swarm Optimization2020 International Conference on Advanced Science and Engineering (ICOASE)10.1109/ICOASE51841.2020.9436547(1-6)Online publication date: 23-Dec-2020
https://doi.org/10.1109/ICOASE51841.2020.9436547
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On the collective classification of email "speech acts"

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Published In

cover image ACM Conferences

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Copyright © 2005 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

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SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Žitnik SGordon Smith G(2023)Automated analysis of postings in fourth grade online discussions to help teachers keep students on-trackInteractive Learning Environments10.1080/10494820.2023.220432732:8(4587-4612)Online publication date: 11-May-2023
https://doi.org/10.1080/10494820.2023.2204327
Zhong L(2022)Deep Neural Networks for Text ClassificationProceedings of the 7th International Conference on Cyber Security and Information Engineering10.1145/3558819.3565095(292-296)Online publication date: 23-Sep-2022
https://dl.acm.org/doi/10.1145/3558819.3565095
Shabat HAbbas N(2020)Enhance the Performance of Independent Component Analysis for Text Classification by Using Particle Swarm Optimization2020 International Conference on Advanced Science and Engineering (ICOASE)10.1109/ICOASE51841.2020.9436547(1-6)Online publication date: 23-Dec-2020
https://doi.org/10.1109/ICOASE51841.2020.9436547
Xue MZhang HLv J(2020)Key Factors of Email Subject GenerationNeural Information Processing10.1007/978-3-030-63820-7_76(668-675)Online publication date: 17-Nov-2020
https://doi.org/10.1007/978-3-030-63820-7_76
Miura YKano RTaniguchi MTaniguchi TMisawa SOhkuma T(2019)Discourse Act Classification Using Discussion Patterns with Neural Networks木構造とグラフ構造を用いたオンライン議論における談話行為の分類Journal of Natural Language Processing10.5715/jnlp.26.5926:1(59-81)Online publication date: 15-Mar-2019
https://doi.org/10.5715/jnlp.26.59
Wang WHosseini SAwadallah ABennett PQuirk CPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Context-Aware Intent Identification in Email ConversationsProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331260(585-594)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331260
Dallachiesa MAggarwal CPalpanas T(2019)Improving Classification Quality in Uncertain GraphsJournal of Data and Information Quality10.1145/324209511:1(1-20)Online publication date: 4-Jan-2019
https://dl.acm.org/doi/10.1145/3242095
Dong BZhang JZhang CYang YYu P(2019)Missing Entity Synergistic Completion across Multiple Isomeric Online Knowledge Libraries2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852141(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852141
Dong BAggarwal CYu P(2019)Transfer Learning for Network Classification2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851772(1-8)Online publication date: Jul-2019
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Dong MYao LWang XBenatallah BZhang XSheng Q(2019)Dual-stream Self-Attentive Random Forest for False Information Detection2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851765(1-8)Online publication date: Jul-2019
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