c○2012 The Association for Computational Linguistics Order copies of this and other ACL proceedings from:

نویسندگان

  • Sophia Ananiadou
  • Galia Angelova
  • Emilia Apostolova
  • Alan Aronson
  • Olivier Bodenreider
  • Wendy Chapman
  • Kevin Cohen
  • Nigel Collier
  • Dina Demner-Fushman
  • Noemie Elhadad
  • Marcelo Fiszman
  • Filip Ginter
  • Su Jian
  • Jin-Dong Kim
  • Zhiyong Lu
  • Aurelie Neveol
  • Jon Patrick
  • John Pestian
  • Sampo Pyysalo
  • Bastien Rance
  • Fabio Rinaldi
  • Thomas Rindflesch
  • Brian Roark
  • Andrey Rzhetsky
  • Daniel Rubin
  • Guergana Savova
  • Hagit Shatkay
  • Matthew Simpson
  • Pontus Stenetorp
  • Yuka Tateisi
  • Jun’ichi Tsujii
  • Yoshimasa Tsuruoka
  • Ozlem Uzuner
  • Karin Verspoor
  • Bonnie Webber
  • Peter White
  • W. John Wilbur
  • Limsoon Wong
  • Antonio Yepes
  • Guodong Zhou
  • Pierre Zweigenbaum
چکیده

Narrative recall tasks are widely used in neuropsychological evaluation protocols in order to detect symptoms of disorders such as autism, language impairment, and dementia. In this paper, we propose a graph-based method commonly used in information retrieval to improve word-level alignments in order to align a source narrative to narrative retellings elicited in a clinical setting. From these alignments, we automatically extract narrative recall scores which can then be used for diagnostic screening. The significant reduction in alignment error rate (AER) afforded by the graph-based method results in improved automatic scoring and diagnostic classification. The approach described here is general enough to be applied to almost any narrative recall scenario, and the reductions in AER achieved in this work attest to the potential utility of this graph-based method for enhancing multilingual word alignment and alignment of comparable corpora for more standard NLP tasks.

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تاریخ انتشار 2012