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

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Computational Linguistics and Intelligent Text Processing (CICLing 2018)

Abstract

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.

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Notes

  1. 1.

    Tool example: http://www.vocapia.com.

  2. 2.

    Available here: http://groups.inf.ed.ac.uk/ami/download/.

  3. 3.

    http://groups.inf.ed.ac.uk/ami/download/.

  4. 4.

    https://github.com/pegahani/AMI-prep.

  5. 5.

    For full results, we refer the readers to: https://github.com/pegahani/Event_detection/blob/master/result/result_4_4.txt.

  6. 6.

    For full results, we refer the readers to: https://github.com/pegahani/Event_detection/blob/master/result/result_4_block_scen.txt.

  7. 7.

    For more results you can visit: https://github.com/pegahani/Event_detection/blob/master/result/Topic_modeling_nmf_block_34_topics.txt.

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Acknowledgement

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

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Correspondence to Bruno Crémilleux .

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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. https://doi.org/10.1007/978-3-031-23793-5_16

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  • DOI: https://doi.org/10.1007/978-3-031-23793-5_16

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