Skip to main content

Text Segmentation Using Light Syntax Parsing and Fuzzy Systems

  • Conference paper
  • First Online:
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation (INFUS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 307))

Included in the following conference series:

Abstract

We present our positions and proposition in regards to the process of text segmentation. The method discussed below aims to improve upon the current processes available for text segmentation by introducing the concept of fuzzy boundaries. our method of segmenting text concerns a population of boundaries to be in a fuzzy state whereby the decision of boundary insertion is determined by a fuzzy system with syntactic information serving as the inputs. Furthermore, we aim to build on this method in the future with the goal of presenting a multifaceted segmentation approach that is applicable across various domains that require segmentation: Text summarisation, Rhetorical structure theory, sentence-based segmentation and paragraph-based segmentation.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Notes

  1. 1.

    We will use segment and sentence interchangeably in this work as theoretically, a sentence can be treated exactly the same as a segment.

References

  1. Ben Ayed, A., Biskri, I., Meunier, J.G.: Automatic text summarization: a new hybrid model based on vector space modelling, fuzzy logic and rhetorical structure analysis. In: Nguyen, N., Chbeir R., Exposito, E., Aniorte, P., Trawiński, B. (eds.) Computational Collective Intelligence. ICCCI 2019. LNCS, vol. 11684, pp. 26–34. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28374-2_3

  2. Beeferman, D., Berger, A., Lafferty, J.: Statistical models for text segmentation. Mach. Learn. 34(1–3), 177–210 (1999)

    Article  Google Scholar 

  3. Choi, F.Y.: Advances in domain independent linear text segmentation. arXiv preprint cs/0003083 (2000)

    Google Scholar 

  4. Dias, G., Alves, E., Lopes, J.G.P.: Topic segmentation algorithms for text summarization and passage retrieval: an exhaustive evaluation. AAAI 7, 1334–1340 (2007)

    Google Scholar 

  5. Hearst, M.A.: Texttiling: A quantitative approach to discourse segmentation. Technical report, Citeseer (1993)

    Google Scholar 

  6. Le Thanh, H., Abeysinghe, G., Huyck, C.: Automated discourse segmentation by syntactic information and cue phrases. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2004), Innsbruck, Austria, pp. 411–415 (2004)

    Google Scholar 

  7. Li, J., Sun, A., Joty, S.R.: Segbot: a generic neural text segmentation model with pointer network. In: IJCAI, pp. 4166–4172 (2018)

    Google Scholar 

  8. Louis, A., Nenkova, A.: A coherence model based on syntactic patterns. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1157–1168 (2012)

    Google Scholar 

  9. Magerman, D.M.: Statistical decision-tree models for parsing. In: Proceedings of the 33rd annual meeting on Association for Computational Linguistics, pp. 276–283. Association for Computational Linguistics (1995)

    Google Scholar 

  10. Mann, W.C., Thompson, S.A.: Rhetorical structure theory: Toward a functional theory of text organization. Text 8(3), 243–281 (1988)

    Google Scholar 

  11. Pevzner, L., Hearst, M.A.: A critique and improvement of an evaluation metric for text segmentation. Comput. Linguist. 28(1), 19–36 (2002)

    Article  Google Scholar 

  12. Scaiano, M., Inkpen, D.: Getting more from segmentation evaluation. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 362–366 (2012)

    Google Scholar 

  13. Soricut, R., Marcu, D.: Sentence level discourse parsing using syntactic and lexical information. In: Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pp. 228–235 (2003)

    Google Scholar 

  14. Tofiloski, M., Brooke, J., Taboada, M.: A syntactic and lexical-based discourse segmenter. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 77–80 (2009)

    Google Scholar 

  15. Wan, S., Paris, C.: Experimenting with clause segmentation for text summarization. In: TAC. Citeseer (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Omar Ali .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ali, O., Gegov, A., Haig, E., Khusainov, R. (2022). Text Segmentation Using Light Syntax Parsing and Fuzzy Systems. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol 307. Springer, Cham. https://doi.org/10.1007/978-3-030-85626-7_5

Download citation

Publish with us

Policies and ethics