Identifying Interpersonal Distance using Systemic Features

  • Casey Whitelaw
  • Jon Patrick
  • Maria Herke-Couchman
Part of the The Information Retrieval Series book series (INRE, volume 20)


This chapter uses Systemic Functional Linguistic (SFL) theory as a basis for extracting semantic features of documents. We focus on the pronominal and determination system and the role it plays in constructing interpersonal distance. By using a hierarchical system model that represents the author’s language choices, it is possible to construct a richer and more informative feature representation with superior computational efficiency than the usual bag-of-words approach. Experiments within the context of financial scam classification show that these systemic features can create clear separation between registers with different interpersonal distance. This approach is generalizable to other aspects of attitude and affect that have been modelled within the systemic functional linguistic theory.


interpersonal distance document classification machine learning feature representation systemic functional linguistics register 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

6. Bibliography

  1. Argamon, S. and Dodick, J. Conjunction and Modal Assessment in Genre Classification: A Corpus-Based Study of Historical and Experimental Science Writing. In this volume, Shanahan J. G., Qu Y., Wiebe J. (Eds.) Computing Attitude and Affect in Text. Springer, Berlin.Google Scholar
  2. Biggs, B. (1990) In the Beginning, The Oxford Illustrated History of New Zealand. Oxford University Press, Oxford.Google Scholar
  3. Biggs, B. (1997) He Whirwhiringa Selected Readings in Maori. Auckland University Press, Auckland.Google Scholar
  4. Couchman, M. (2001) Transposing culture: A tri-stratal exploration of the meaning making of two cultures. Honours thesis, Macquarie University.Google Scholar
  5. Eggins, S., Wignell, P. and Martin, J. R. (1993) Register analysis: theory and practice. In The discourse of history: distancing the recoverable past, 75–109, Pinter, London.Google Scholar
  6. Halliday, M. A. K. and Hasan, R. (1985) Language, Context and Text: a social semiotic perspective. Geelong, Victoria: Deakin University Press.Google Scholar
  7. Halliday, M. A. K. (1994) Introduction to Functional Grammar. Edward Arnold, second edition.Google Scholar
  8. Halliday, M. A. K. (1995) Computing Meaning: some reflections on past experience and present prospects. Paper presented to PACLING95, Brisbane, April, 1995.Google Scholar
  9. Hasan, R. (1996) Ways of saying, ways of meaning: selected papers of Ruqaiya Hasan. Cassell, London.Google Scholar
  10. Herke-Couchman, M. A. (2003) Arresting the scams: Using systemic functional theory to solve a hi-tech social problem. In Australian SFL Association Conference 2003.Google Scholar
  11. Iedema, R., Feez, S. and White, P. (1995) Media literacy. Sydney, Metropolitan East Disadvantaged Schools Program.Google Scholar
  12. Kessler, B., Nunberg, G., and Shutze, H. (1997) Automatic detection of text genre. In Philip R. Cohen and Wolfgang Wahlster (Eds), Proceedings of the Thirty-Fifth Annual Meeting of the ACL and Eigth Conference of the EACL, 32–38, Somerset, New Jersey.Google Scholar
  13. Mann, W. C. and Matthiessen, C. M. I. M. (1985). Nigel: A systemic grammar for text generation. In Benson, R. and Greaves, J. (Eds), Systemic Perspectives on Discourse: Selected Papers from the 9th International Systemic Workshop. Ablex.Google Scholar
  14. Mann, W. C. and Thompson, S. A. (1988) Rhetorical structure theory: Toward a functional theory of text organisation. Text, 8(3), 243–281.Google Scholar
  15. Martin, J. R. and Rose, D. (2003). Working with Discourse: Meaning Beyond the Clause. Continuum, London and New York.Google Scholar
  16. Martin, J. R. (2004) Mourning: how we get aligned. In Discourse & Society 15.2/3 (Special Issue on ‘Discourse around 9/11’). 321–344.Google Scholar
  17. Matthiessen, C. M. I. M (1993) Register analysis: theory and practice. In Register in the round: diversity in a unified theory of register. 221–292. Pinter, London.Google Scholar
  18. Matthiessen, C. M. I. M (1995) Lexico-grammatical cartography: English systems. International Language Sciences Publishers.Google Scholar
  19. Matthiessen, C. M. I. M. (2003). Frequency Profiles of some basic grammatical systems: an interim report. Macquarie University.Google Scholar
  20. Munro, R. (2003) Towards a computational inference and application of a functional grammar, Honours thesis, University of Sydney, 2003.Google Scholar
  21. O’Donnell, M. (1994) Sentence analysis and generation: a systemic perspective. PhD thesis, University of Sydney.Google Scholar
  22. O’Donnell, M. (2002) Automating the coding of semantic patterns: applying machine learning to corpus linguistics. In Proceedings of the 29th International Systemic Functional Workshop. University of Liverpool.Google Scholar
  23. Quinlan, J. R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann.Google Scholar
  24. Riloff, E., Wiebe, J. and Wilson, T. (2003), Learning subjective nouns using extraction pattern bootstrapping. In Proceedings of CoNLL-2003, 25–32. Edmonton, Canada.Google Scholar
  25. Sebastiani, F. (2002) Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1–47.CrossRefGoogle Scholar
  26. Sebastiani, F. (2004). Text Categorization. In Alessandro Zanasi (Ed.), Text Mining and its Applications, WIT Press, Southampton, UK.Google Scholar
  27. Taboada, M. and Grieve, J. Analysing Appraisal Automatically. In Shanahan J. G., Qu Y., Wiebe J. (Eds.) Computing Attitude and Affect in Text. Springer, Berlin.Google Scholar
  28. Turney, P. (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings 40th Annual Meeting of the ACL (ACL’02), 417–424Google Scholar
  29. Wiebe, J. (1990) Recognizing Subjective Sentences: A Computational Investigation of Narrative Text. PhD thesis, State University of New York at Buffalo.Google Scholar
  30. Witten, I.H. and Eibe, F. (1999) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann.Google Scholar
  31. Whitelaw, C. and Argamon, S. (2004) Systemic Functional Features for Stylistic Text Categorization. In Proceedings of the AAAI 2004 Fall Symposium on Style and Meaning in Language, Art, Music, and Design. AAAI Press.Google Scholar
  32. Wu, C. (2000) Modelling Linguistic Resources: A Systemic Functional Approach. Unpublished PhD thesis, Macquarie University, Sydney.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Casey Whitelaw
    • 1
  • Jon Patrick
    • 1
  • Maria Herke-Couchman
    • 2
  1. 1.Language Technology Research Group, School of Information TechnologiesUniversity of SydneyAustralia
  2. 2.Centre for Language in Social Life, Division of Linguistics and PsychologyMacquarie UniversityAustralia

Personalised recommendations