Methods of automatic knowledge acquisition (KA), although widely used in NLP [5, 10], are not shown to be useful for capturing tacit and common-sense knowledge, because this knowledge is not commonly derivable from grammatical and structural properties of texts available in linguistic corpora [14].
According to Hasan (1984), "the basis for textual coherence lies in cohesion" (p. 210), and "Cohesion is an essential property of texts, but it is the way the cohesive resources are deployed that makes the difference between text and non-text, and between one text and another" (Halliday and Hasan 1985 [1989], p. 54).
Railton (1981) suggests that an explanatory claim provides information about an underlying ideal text if the former reduces uncertainty about some of the properties of the text, in the sense of ruling in or out various possibilities concerning its structure.
A key concept informing document analysis as a form of discourse involves examining the formal properties of a text, including the vocabulary choices and the grammatical features of the text (Fairclough 2003).
Adapting this characterisation into functional linguistics, if the inter-stratal relation can be described in terms of supervenience, then we should see similar dependencies between higher and lower level properties of a text.
More recently, Loukina et al. (2016) conducted a study to investigate the extent to which textual properties of a text affect the difficulty of listening items in the English test.
After assigning stroke widths to all image pixels, the SWT method groups pixels with similar stroke widths into connected components and filters out those that violate geometrical properties of the text.
Its presence seems to depend on (at least) the language being read, visual properties of the text (e.g., initial letter capitalization), the type of relationship between preview and target, and as shown here, semantic constraint generated by the prior sentence context.
Canipre, well aware of the ominous properties of red text.
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