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Statistically constrained shallow text marking: techniques, evaluation paradigm, and resultsFormat | Member Price | Non-Member Price |
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Paper Abstract
We present three natural language marking strategies based on fast and reliable shallow parsing techniques, and on widely
available lexical resources: lexical substitution, adjective conjunction swaps, and relativiser switching. We test these
techniques on a random sample of the British National Corpus. Individual candidate marks are checked for goodness of
structural and semantic fit, using both lexical resources, and the web as a corpus. A representative sample of marks is given
to 25 human judges to evaluate for acceptability and preservation of meaning. This establishes a correlation between corpus
based felicity measures and perceived quality, and makes qualified predictions. Grammatical acceptability correlates with
our automatic measure strongly (Pearson's r = 0.795, p = 0.001), allowing us to account for about two thirds of variability
in human judgements. A moderate but statistically insignificant (Pearson's r = 0.422, p = 0.356) correlation is found with
judgements of meaning preservation, indicating that the contextual window of five content words used for our automatic
measure may need to be extended.
Paper Details
Date Published: 27 February 2007
PDF: 9 pages
Proc. SPIE 6505, Security, Steganography, and Watermarking of Multimedia Contents IX, 65050Z (27 February 2007); doi: 10.1117/12.713355
Published in SPIE Proceedings Vol. 6505:
Security, Steganography, and Watermarking of Multimedia Contents IX
Edward J. Delp III; Ping Wah Wong, Editor(s)
PDF: 9 pages
Proc. SPIE 6505, Security, Steganography, and Watermarking of Multimedia Contents IX, 65050Z (27 February 2007); doi: 10.1117/12.713355
Show Author Affiliations
Brian Murphy, Trinity College Dublin (Ireland)
Carl Vogel, Trinity College Dublin (Ireland)
Published in SPIE Proceedings Vol. 6505:
Security, Steganography, and Watermarking of Multimedia Contents IX
Edward J. Delp III; Ping Wah Wong, Editor(s)
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