Skip to main content

An Experimental Approach for Information Extraction in Multi-party Dialogue Discourse

  • Conference paper
  • First Online:
Computational Linguistics and Intelligent Text Processing (CICLing 2018)


In this paper, we address the task of information extraction for transcript meetings. Meeting documents are not usually well structured and are lacking formatting and punctuations. In addition, the information are distributed over multiple sentences. We experimentally investigate the usefulness of numerical statistics and topic modelling methods on a real dataset containing multi-part dialogue texts. Such information extraction can be used for different tasks, of which we consider two: contrasting thematically related but distinct meetings from each other, and contrasting meetings involving the same participants from those involving other. In addition to demonstrating the difference between counting and topic modeling results, we also evaluate our experiments with respect to the gold standards provided for the dataset.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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

Institutional subscriptions

Similar content being viewed by others


  1. 1.

    Tool example:

  2. 2.

    Available here:

  3. 3.

  4. 4.

  5. 5.

    For full results, we refer the readers to:

  6. 6.

    For full results, we refer the readers to:

  7. 7.

    For more results you can visit:


  1. Augmented multi-party interaction (2010).

  2. Carletta, J.: Unleashing the killer corpus: experiences in creating the multi-everything AMI meeting corpus. Lang. Resour. Eval. 41, 181–190 (2007)

    Article  Google Scholar 

  3. Fernández, R., Frampton, M., Dowding, J., Adukuzhiyil, A., Ehlen, P., Peters, S.: Identifying relevant phrases to summarize decisions in spoken meetings. In: Proceedings of Interspeech 2008, Brisbane (2008)

    Google Scholar 

  4. Fernández, R., Frampton, M., Ehlen, P., Purver, M., Peters, S.: Modelling and detecting decisions in multi-party dialogue. In: Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue, SIGdial 2008, pp. 156–163. Association for Computational Linguistics, Stroudsburg, PA, USA (2008)

    Google Scholar 

  5. Galley, M., McKeown, K., Fosler-Lussier, E., Jing, H.: Discourse segmentation of multi-party conversation. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, ACL 2003, vol. 1. Association for Computational Linguistics, Stroudsburg, PA, USA (2003)

    Google Scholar 

  6. Georgescul, M., Clark, A., Armstrong, S.: Exploiting structural meeting-specific features for topic segmentation. In: TALN/RECITAL, Toulouse (France), pp. 15–24 (2007)

    Google Scholar 

  7. Gurin, Y., Szymanski, T., Keane, M.T.: Discovering news events that move markets. In: Intelligent Systems Conference 2017 (IntelliSys2017), London, United Kingdom, 7–8 Sept 2017 (2017)

    Google Scholar 

  8. He, Q., Chang, K., Lim, E.P.: Analyzing feature trajectories for event detection. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 207–214. ACM, New York (2007)

    Google Scholar 

  9. Kleinberg, J.M.: Bursty and hierarchical structure in streams. Data Min. Knowl. Discov. 7(4), 373–397 (2003).

  10. Lau, J.H., Collier, N., Baldwin, T.: On-line trend analysis with topic models:#twitter trends detection topic model online. Proc. COLING 2012, 1519–1534 (2012)

    Google Scholar 

  11. Lee, D.D., Seung, H.S.: Learning the parts of objects by nonnegative matrix factorization. Nature 401, 788–791 (1999)

    Article  Google Scholar 

  12. Purver, M., Dowding, J., Niekrasz, J., Ehlen, P., Noorbaloochi, S., Peters, S.: Detecting and summarizing action items in multi-party dialogue. In: In Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue (2007)

    Google Scholar 

  13. Purver, M., Griffiths, T.L., Körding, K.P., Tenenbaum, J.B.: Unsupervised topic modelling for multi-party spoken discourse. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, ACL-44, pp. 17–24. Association for Computational Linguistics, Stroudsburg, PA, USA (2006)

    Google Scholar 

  14. Ramage, D., Dumais, S.T., Liebling, D.J.: Characterizing microblogs with topic models. In: ICWSM, vol. 10(1), pp. 16 (2010)

    Google Scholar 

  15. Řehůřek, R., Sojka, P.: Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, Malta (2010).

  16. Riedhammer, K., Favre, B., Hakkani-Tür, D.: Packing the Meeting Summarization Knapsack. In: Interspeech, Brisbane (Australia). Unknown, Unknown or Invalid Region (2008).

  17. Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams. In: In Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2009). AAAI (2009)

    Google Scholar 

  18. Tur, G., et al.: The CALO meeting speech recognition and understanding system. In: 2008 IEEE Spoken Language Technology Workshop, pp. 69–72 (2008)

    Google Scholar 

  19. Tur, G., et al.: The calo meeting assistant system. IEEE Trans. Audio Speech Lang. Process. 18, 1601–1611 (2010)

    Article  Google Scholar 

  20. Weng, J., Lee, B.S.: Event detection in twitter. In: ICWSM, vol. 11, pp. 401–408 (2011)

    Google Scholar 

Download references


This work is supported by the FUI 22 (REUs project) and the ANR (French Research National Agency) funded project NARECA ANR-13-CORD-0015.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Bruno Crémilleux .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alizadeh, P., Cellier, P., Charnois, T., Crémilleux, B., Zimmermann, A. (2023). An Experimental Approach for Information Extraction in Multi-party Dialogue Discourse. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13396. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23792-8

  • Online ISBN: 978-3-031-23793-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics