An account of the GDEX tool for finding good examples in a corpus, explaining how this works and how successful it is.
The previous exercise will have given you an idea of what makes a good dictionary example.
Evaluating sentences like the ones in Step 6.7 helps us get a clearer understanding of the factors that go into making good (and bad) examples. Similar thinking informed the development of a software tool called ‘GDEX’ (standing for ‘Good Dictionary EXamples’) which is designed to automatically find good examples in a corpus.
The developers started from a set of characteristics which are typical of good examples, including:
They shouldn’t be too long.
They should be easy to understand, avoiding rare or technical vocabulary and distracting names of people or organisations.
They should illustrate the most typical ways a word is used, such as its normal grammar patterns and collocates (the words it most often occurs with).
They should be as self-contained as possible – for example, by avoiding pronouns which refer back to something in a previous sentence.
These conditions were then translated into specific, measurable features, such as sentence length (not too short, not too long); frequencies of other words in the sentence (to avoid anything too rare); number of pronouns in the sentence (these can be confusing if you don’t know what they refer to); and the appearance of common collocates (using data in the Word Sketches). Each feature was given a ‘weighting’. The system then went through sentences that included the search word and gave each sentence a score based on these criteria. The ones with the best scores were then ‘promoted’, so that in a concordance for ‘demonstrate’, for example, the ‘best’ examples appear at the top. This gives the lexicographer a candidate set of potential dictionary examples to choose from.
The GDEX algorithm has been used in a number of dictionary projects, with considerable success. The system doesn’t always get it right, and in a given set of 10 best examples, there will usually be two or three which are definitely not suitable. But it is still being improved, and it is already a more cost-effective way of finding examples than simply asking lexicographers to scan dozens or hundreds of corpus sentences.