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Argumentative Zoning Applied to Critiquing Novices’ Scientific Abstracts

  • Valéria D. Feltrim
  • Simone Teufel
  • Maria Graças V. das Nunes
  • Sandra M. Aluísio
Part of the The Information Retrieval Series book series (INRE, volume 20)

Abstract

We present a system that applies Argumentative Zoning (AZ) (Teufel and Moens, 2002), a method of determining argumentative structure in texts, to the task of advising novice graduate writers on their writing. For this task, it is important to automatically determine the rhetorical/argumentative status of a given sentence in the text. On the basis of this information, users can be advised that a different sentence order might be more advantageous or that certain argumentative moves are missing. In implementing such a system, we had to port AZ from English to Portuguese, as our system is designed to help the writing of Brazilian PhD theses in Computer Science. In this chapter, we report on the overall system, named SciPo, the porting exercise, including a human annotation experiment to verify the reproducibility of our annotation scheme, and the intrinsic and extrinsic evaluation of the AZ module of the system.

Keywords

academic writing Argumentative Zoning machine learning 

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7. Bibliography

  1. Aires, R. V. X., Aluísio, S. M., Kuhn, D. C. S., Andreeta, M. L. B. and Oliveira Jr., O. N. (2000) Combining Multiple Classifiers to Improve Part of Speech Tagging: A Case Study for Brazilian Portuguese. In Proceeding of SBIA 2000. Atibaia, SP, Brazil.Google Scholar
  2. Aluísio, S.M., Barcelos, I., Sampaio, J. and Oliveira Jr., O. (2001) How to learn the many unwritten “Rules of the Game” of the Academic Discourse: A hybrid Approach based on Critiques and Cases. In Proceedings of the IEEE International Conference on Advanced Learning Technologies. 257–260. Madison/Wisconsin.Google Scholar
  3. Aluísio, S.M. and Oliveira Jr., O.N. (1996) A Detailed Schematic Structure of Research Papers Introductions: An Application in Support-Writing Tools. Revista de la Sociedad Espanyola para el Procesamiento del Lenguaje Natural, 19, 141–147.Google Scholar
  4. Anthony, L. and Lashkia, G.V. (2003) Mover: A Machine Learning Tool to Assist in the Reading and Writing of Technical Papers. IEEE Transactions on Professional Communication, 46(3),185–193.CrossRefGoogle Scholar
  5. Broady, E. and Shurville, S. (2000) Developing Academic Writer: Designing a Writing Environment for Novice Academic Writers. In E. Broady (Ed.) Second Language Writing in a Computer Environment. 131–151. CILT, London.Google Scholar
  6. Burstein, J., Marcu, D. and Knight, K. (2003) Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays. IEEE Intelligent Systems: Special Issue on Natural Language Processing, 18(1), 32–39.Google Scholar
  7. Feltrim, V., Aluísio, S. and Nunes, M.G.V. (2003) Analysis of the rhetorical structure of computer science abstracts in Portuguese. In Proceedings of the Corpus Linguistics 2003, Dawn Archer, Paul Rayson, Andrew Wilson and Tony McEnery (eds.), UCREL Technical Papers, Vol. 16, Part 1, Special Issue (2003) 212–218.Google Scholar
  8. Kriegsman, M. and Barletta, R. (1993) Building a Case-based Help Desk Application. IEEE Expert, December, 18–26.Google Scholar
  9. Liddy, E.D. (1991) The Discourse-Level Structure of Empirical Abstracts: An Exploratory Study. Information Processing & Management, 27(1), 55–81.CrossRefGoogle Scholar
  10. Narita, M. (2000) Corpus-based English Language Assistant to Japanese Software Engineers. In Proceedings of MT-2000 Machine Translation and Multilingual Applications in the New Millennium. 24-1–24-8.Google Scholar
  11. Santos M. (1996) The textual organisation of research paper abstracts. Text, 16(4), 481–499.Google Scholar
  12. Sharples, M., Goodlet, J. and Clutterbuck, A. (1994) A comparison of algorithms for hypertext notes network linearization. International Journal of Human-Computer Studies, 40(4), 727–752.CrossRefGoogle Scholar
  13. Sharples, M. and Pemberton, L. (1992) Representing writing: external representations and the writing process. In P.O. Holt and N. Williams (Eds.) Computers and Writing: State of the Art. 319–336. Intellect, Oxford.Google Scholar
  14. Siegel, S. and Castellan, N. (1988) Nonparametric Statistics for the Behavioral Sciences, McGraw-Hill.Google Scholar
  15. Swales, J.M. (1990) Genre Analysis: English in Academic and Research Settings, Cambridge University Press. Cambridge, UK.Google Scholar
  16. Teufel, S. and Moens, M. (2002) Summarising Scientific Articles — Experiments with Relevance and Rhetorical Status. Computational Linguistics, 28(4), 409–446.CrossRefGoogle Scholar
  17. Teufel, S. and Moens, M. (2000) What’s yours and what’s mine: Determining Intellectual Attribution in Scientific Text. In Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. Hong Kong.Google Scholar
  18. Teufel, S., Carletta, J. and Moens, M. (1999) An annotation scheme for discourse-level argumentation in research articles. In Proceedings of the Ninth Meeting of the European Chapter of the Association for Computational Linguistics (EACL-99), 110–117.Google Scholar
  19. Weissberg, R. and Buker, S. (1990) Writing up Research: Experimental Research Report Writing for Students of English, Prentice Hall.Google Scholar
  20. Witten, I. and Frank, E. (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Valéria D. Feltrim
    • 1
  • Simone Teufel
    • 2
  • Maria Graças V. das Nunes
    • 1
  • Sandra M. Aluísio
    • 1
  1. 1.NILC,ICMCUniversidade de São PauloSão Carlos, SPBrazil
  2. 2.Computer LaboratoryUniversity of CambridgeCambridgeUK

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