Advertisement

Research on Language and Computation

, Volume 4, Issue 1, pp 77–109 | Cite as

Syntactic Simplification and Text Cohesion

  • Advaith Siddharthan
Article

Abstract

Syntactic simplification is the process of reducing the grammatical complexity of a text, while retaining its information content and meaning. The aim of syntactic simplification is to make text easier to comprehend for human readers, or process by programs. In this paper, we formalise the interactions that take place between syntax and discourse during the simplification process. This is important because the usefulness of syntactic simplification in making a text accessible to a wider audience can be undermined if the rewritten text lacks cohesion. We describe how various generation issues like sentence ordering, cue-word selection, referring-expression generation, determiner choice and pronominal use can be resolved so as to preserve conjunctive and anaphoric cohesive relations during syntactic simplification and present the results of an evaluation of our syntactic simplification system.

Keywords

anaphoric structure cue-word selection determiner choice discourse structure sentence ordering syntactic simplification text cohesion 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Canning Y. (2002) Syntactic Simplification of Text. PhD thesis, University of Sunderland, UKGoogle Scholar
  2. Canning Y., Tait J., Archibald J., Crawley R. (2000) Cohesive Generation of Syntactically Simplified Newspaper Text. In Sojka P., Kipecek I., Pala K. (eds). Text, Speech and Dialogue: Third International Workshop (TSD’00). Lecture Notes in Artificial Intelligence 1902. Springer-Verlag, Brno, Czech Republic, pp. 145–150CrossRefGoogle Scholar
  3. Caplan D. (1992) Language: Structure, Processing, and Disorders. MIT Press, Cambridge, MassachusettsGoogle Scholar
  4. Carroll J., Minnen G., Pearce D., Canning Y., Devlin S., Tait J. (1999) Simplifying English text for Language Impaired Readers. Proceedings of the 9th Conference of the European Chapter of the Association for Computational Linguistics (EACL’99). Bergen. Norway, pp. 269–270Google Scholar
  5. Chandrasekar R., Doran C., Srinivas B. (1996) Motivations and Methods for Text Simplification. Proceedings of the 16th International Conference on Computational Linguistics (COLING ’96). Copenhagen, Denmark, pp. 1041–1044.Google Scholar
  6. Chandrasekar R., Srinivas B. (1997) Automatic Induction of Rules for Text Simplification. Knowledge-Based Systems 10:183–190CrossRefGoogle Scholar
  7. Devlin S. (1999) Simplifying Natural Language for Aphasic Readers. PhD thesis, University of Sunderland, UKGoogle Scholar
  8. Grosz B., Joshi A., Weinstein S. (1995) Centering: A Framework for Modelling the Local Coherence of Discourse. Computational Linguistics 21(2):203–226Google Scholar
  9. Grosz B., Sidner C. (1986) Attention, Intentions, and the Structure of Discourse. Computational Linguistics 12(3):175–204Google Scholar
  10. Grover C., Matheson C., Mikheev A., Moens M. (2000) LT TTT – A Flexible Tokenisation Tool. Proceedings of Second International Conference on Language Resources and Evaluation, Athens, Greece, pp. 1147–1154.Google Scholar
  11. Van Hentenryck P. (1989) Constraint Satisfaction in Logic Programming. MIT Press, MassGoogle Scholar
  12. Mann W.C., Thompson S.A. (1988) Rhetorical Structure Theory: Towards a functional theory of text organization. Text 8(3):243–281Google Scholar
  13. Miller G.A., Beckwith R., Fellbaum C.D., Gross D., Miller K. (1993) Five Papers on WordNet. Technical report. Princeton University, Princeton, NJGoogle Scholar
  14. Parr S. (1993) Aphasia and Literacy. PhD thesis, University of Central England.Google Scholar
  15. Power R. (2000) Planning Texts by Constraint Satisfaction. Proceedings of the 18th International Conference on Computational Linguistics (COLING 2000). Saarbrücken, Germany, pp. 642–648.Google Scholar
  16. Quigley S.P., Paul P.V. (1984) Language and Deafness. College-Hill Press, San DiegoGoogle Scholar
  17. Scott D., de Souza C.S. Getting the Message Across in RST-based Text Generation. In Mellish C., Zock M. (eds.), Current Research in Natural Language Generation. Academic Press pp. 47–73.Google Scholar
  18. Siddharthan A. (2002) Resolving Attachment and Clause Boundary Ambiguities for Simplifying Relative Clause Constructs. Proceedings of the Student Workshop, 40th Meeting of the Association for Computational Linguistics (ACL’02), Philadelphia, USA, pp. 60–65.Google Scholar
  19. Siddharthan A. (2003a) Resolving Pronouns robustly: Plumbing the Depths of Shallowness. Proceedings of the Workshop on Computational Treatments of Anaphora, 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL’03), Budapest, Hungary, pp. 7–14.Google Scholar
  20. Siddharthan A. (2003b) Syntactic Simplification and Text Cohesion. PhD thesis, University of Cambridge, UKGoogle Scholar
  21. Siddharthan A., Copestake A. (2004) Generating Referring Expressions in Open Domains. To appear in Proceedings of the 42th Meeting of the Association for Computational Linguistics Annual Conference (ACL 2004), Barcelona, Spain.Google Scholar
  22. Williams S., Reiter E., Osman L. (2003) Experiments with Discourse-level Choices and Readability. Proceedings of the European Natural Language Generation Workshop (ENLG), 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL’03), Budapest, Hungary, pp. 127–134.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.Department of Computer ScienceColumbia UniversityNew YorkUSA

Personalised recommendations