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.
Many plain text information hiding techniques demand deep semantic processing, and so suffer in reliability. In contrast,
syntactic processing is a more mature and reliable technology. Assuming a perfect parser, this paper evaluates a set of
automated and reversible syntactic transforms that can hide information in plain text without changing the meaning or
style of a document. A large representative collection of newspaper text is fed through a prototype system. In contrast to
previous work, the output is subjected to human testing to verify that the text has not been significantly compromised by
the information hiding procedure, yielding a success rate of 96% and bandwidth of 0.3 bits per sentence.
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