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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)

Abstract

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.

Keywords

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

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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

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