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Related Experiment Video

Updated: Jan 20, 2026

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Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection

Aitziber Atutxa1, Kepa Bengoetxea1, Arantza Diaz de Ilarraza2

  • 1Ixa Group, Language and Computer Systems, University of the Basque Country (UPV/EHU), Bilbao, Basque Country.

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Summary

This study introduces a new tool for discourse analysis, improving text segmentation and identifying the Central Unit. This aids natural language processing (NLP) tasks by enhancing inter-sentence relation detection.

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing (NLP)

Background:

  • Discourse structure analysis is crucial for various NLP tasks like opinion mining and summarization.
  • Current discourse parsers excel at intra-sentence relations but struggle with inter-sentence connections.
  • Identifying a Central Unit aids in improving rhetorical labeling and discourse analysis accuracy.

Purpose of the Study:

  • To develop the initial stages of a discourse parser using a top-down strategy.
  • To create a tool for automatic text segmentation and Central Unit detection.
  • To lay the groundwork for future advancements in assigning rhetorical relations.

Main Methods:

  • Implemented a top-down strategy for discourse parsing.
  • Developed a discourse segmenter to identify basic discourse units.
  • Created an automatic Central Unit detector to pinpoint the main discourse component.

Main Results:

  • Successfully built a tool combining a discourse segmenter and a Central Unit detector.
  • The tool addresses the challenge of accurately capturing inter-sentence discourse relations.
  • Provides a foundation for more sophisticated discourse analysis and rhetorical labeling.

Conclusions:

  • The developed tool represents a significant step towards more accurate discourse parsing.
  • Automatic Central Unit detection enhances the capabilities of NLP systems.
  • Future work will focus on integrating rhetorical relation assignment into the parser.