Skip to main content
SpringerLink
Log in
Book cover

European Conference on Information Retrieval

ECIR 2018: Advances in Information Retrieval pp 114–126Cite as

Bringing Back Structure to Free Text Email Conversations with Recurrent Neural Networks

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10772))

Abstract

Email communication plays an integral part of everybody’s life nowadays. Especially for business emails, extracting and analysing these communication networks can reveal interesting patterns of processes and decision making within a company. Fraud detection is another application area where precise detection of communication networks is essential. In this paper we present an approach based on recurrent neural networks to untangle email threads originating from forward and reply behaviour. We further classify parts of emails into 2 or 5 zones to capture not only header and body information but also greetings and signatures. We show that our deep learning approach outperforms state-of-the-art systems based on traditional machine learning and hand-crafted rules. Besides using the well-known Enron email corpus for our experiments, we additionally created a new annotated email benchmark corpus from Apache mailing lists.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://mail-archives.apache.org/mod_mbox/.

  2. 2.

    https://github.com/mailgun/talon.

  3. 3.

    The quagga is a subspecies of zebras. (https://en.wikipedia.org/wiki/Quagga).

  4. 4.

    http://mail-archives.apache.org/mod_mbox/.

  5. 5.

    Annotated datasets and code can be found at https://github.com/TimRepke/Quagga.

  6. 6.

    http://www.cs.cmu.edu/~vitor/software/jangada/.

  7. 7.

    http://minorthird.sourceforge.net/.

  8. 8.

    http://zebra.thoughtlets.org/zoning.php.

  9. 9.

    https://github.com/gerhardgossen/soZebra.

References

  1. Bonchi, F., Castillo, C., Gionis, A., Jaimes, A.: Social network analysis and mining for business applications. TIST 2(3), 22 (2011)

    Article  Google Scholar 

  2. Carvalho, V., Cohen, W.: Learning to extract signature and reply lines from email. In: CEAS (2004)

    Google Scholar 

  3. Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. CoRR (2014)

    Google Scholar 

  4. Estival, D., Gaustad, T., Pham, S., Radford, W., Hutchinson, B.: Author profiling for English emails. In: Conference of the Pacific ACL (2007)

    Google Scholar 

  5. Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. CoRR (2015)

    Google Scholar 

  6. Jamison, E., Gurevych, I.: Headerless, quoteless, but not hopeless? Using pairwise email classification to disentangle email threads. In: RANLP (2013)

    Google Scholar 

  7. Joty, S., Carenini, G., Ng, R.T.: Topic segmentation and labeling in asynchronous conversations. Artif. Intell. Res. 47, 521–573 (2013)

    MathSciNet  MATH  Google Scholar 

  8. Kim, Y., Jernite, Y., Sontag, D., Rush, A.: Character-aware neural language models. CoRR (2015)

    Google Scholar 

  9. Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22

    Chapter  Google Scholar 

  10. Lampert, A., Dale, R., Paris, C.: Segmenting email message text into zones. In: EMNLP (2009)

    Google Scholar 

  11. Lampert, A., Dale, R., Paris, C.: Detecting emails containing requests for action. In: Human Language Technologies. ACL (2010)

    Google Scholar 

  12. Lang, K.: Newsweeder: learning to filter netnews. In: Twelfth International Conference on Machine Learning (1995)

    Google Scholar 

  13. Ma, X., Hovy, E.H.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. CoRR (2016)

    Google Scholar 

  14. Mujtaba, G., Shuib, L., Raj, R., Majeed, N., Al-Garadi, M.: Email classification research trends: review and open issues. IEEE Access 5, 9044–9064 (2017)

    Article  Google Scholar 

  15. Oard, D., Webber, W., Kirsch, D., Golitsynskiy, S.: Avocado Research Email Collection. Linguistic Data Consortium, Philadelphia (2015)

    Google Scholar 

  16. Perer, A., Shneiderman, B.: Beyond threads: identifying discussions in email archives. Technical report, MUC (2005)

    Google Scholar 

  17. Rauscher, F., Matta, N., Atifi, H.: Context aware knowledge zoning: traceability and business emails. In: Mercier-Laurent, E., Boulanger, D. (eds.) AI4KM 2015. IAICT, vol. 497, pp. 66–79. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55970-4_5

    Chapter  Google Scholar 

  18. Scerri, S., Gossen, G., Davis, B., Handschuh, S.: Classifying action items for semantic email. In: LREC (2010)

    Google Scholar 

  19. Nguyen, D.T., Joty, S., Boussaha, B.E.A., de Rijke, M.: Thread reconstruction in conversational data using neural coherence models. In: Neu-IR (2017)

    Google Scholar 

  20. Wang, Y.C., Joshi, M., Cohen, W.W., Rosé, C.P.: Recovering implicit thread structure in newsgroup style conversations. In: ICWSM (2008)

    Google Scholar 

  21. Yang, L., Dumais, S., Bennett, P., Awadallah, A.: Characterizing and predicting enterprise email reply behavior. In: SIGIR (2017)

    Google Scholar 

  22. Yeh, J., Hamly, A.: Thread reassembly using similary matching. In: CEAS (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hasso Plattner Institute, Potsdam, Germany

    Tim Repke & Ralf Krestel

Authors
  1. Tim Repke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ralf Krestel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tim Repke .

Editor information

Editors and Affiliations

  1. Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  2. LIP6 – UPMC/CNRS, University Pierre et Marie Curie, Paris, France

    Benjamin Piwowarski

  3. University of Glasgow, Glasgow, United Kingdom

    Leif Azzopardi

  4. Technical University of Vienna, Vienna, Austria

    Allan Hanbury

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Repke, T., Krestel, R. (2018). Bringing Back Structure to Free Text Email Conversations with Recurrent Neural Networks. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76941-7_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76940-0

  • Online ISBN: 978-3-319-76941-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation