By Steven Jones
Antonymy is the technical identify used to explain 'opposites', pairs of phrases resembling rich/poor, love/hate and male/female. Antonyms are a ubiquitous a part of daily language, and this booklet offers an in depth, finished account of the phenomenon.This booklet demonstrates how conventional linguistic conception will be revisited, up to date and challenged within the corpus age. it will likely be crucial examining for students attracted to antonymy and corpus linguistics.
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Additional info for Antonymy: A Corpus-Based Perspective (Routledge Advances in Corpus Linguistics)
G. g. Fellbaum 1995 and Kwon 1998). However, the first analysis of antonymy to make use of corpus data was published by Justeson and Katz in 1991. Three years later, Mettinger published a book about the role of antonymy in text. The work of these researchers remains the most important in establishing how antonymy operates in text and their findings will now be outlined in turn. Using antonyms identified by Deese and (primarily) the one-million-word Brown corpus, Justeson and Katz succeed in proving that ‘adjectives do indeed tend to occur in the same sentence as their antonyms far more frequently than expected by chance’ (1991: 142).
Thus, multiple incompatibility may be seen as non-gradable antonymy extended to three or more terms. Whether such examples remain within the boundaries of antonymy is debatable. e. systems with a finite number of members. ‘Seasons of the year’ and ‘cardinal compass points’ are other examples. ‘Open sets’ of incompatibility are thinner on the ground, with the most obvious example being the numerical system. Numbers are incompatible because, for instance, if my age is twenty-seven, my age cannot simultaneously be seventeen or thirty-seven.
A dozen pairs, when sampled proportionately, would have yielded five sentences or fewer. Little of interest could be gleaned about an antonymous pair from such a tiny sample. Ultimately, it was decided that the only way to be sure of keeping the 32 Approaching antonymy afresh database balanced was to select the number of sentences to be sampled for each pair personally. Given that the pairs themselves were chosen largely according to intuitive criteria, there is no obvious reason why the number of sentences retrieved for each pair should not also be determined manually.