Exploring Approaches to Discriminating among Near-Synonyms

Exploring approaches to discriminating among near-synonyms Mary Gardiner Centre for Language Technology Macquarie University gardiner@ics.mq.edu.au Mark Dras Centre for Language Technology Macquarie University madras@ics.mq.edu.au Abstract Near-synonyms are words that mean ap- proximately the same thing, and which tend to be assigned to the same leaf in ontolo- gies such as WordNet. However, they can differ from each other subtly in both mean- ing and usage—consider the pair of near- synonyms frugal and stingy—and therefore choosing the appropriate near-synonym for a given context is not a trivial problem. Initial work by Edmonds (1997) suggested that corpus statistics methods would not be particularly effective, and led to subsequent work adopting methods based on specific lexical resources. In earlier work (Gardiner and Dras, 2007) we discussed the hypoth- esis that some kind of corpus statistics ap- proach may still be effective in some situa- tions, particularly if the near-synonyms dif- fer in sentiment from each other, and we presented some preliminary confirmation of the truth of this hypothesis. This suggests that problems involving this type of near- synonym may be particularly amenable to corpus statistics methods. In this paper we investigate whether this re- sult extends to a different corpus statistics method and in addition we analyse the re- sults with respect to a possible confound- ing factor discussed in the previous work: the skewness of the sets of near synonyms. Our results show that the relationship be- tween success in prediction and the nature of the near-synonyms is method dependent and that skewness is a more significant factor. 1 Introduction Choosing an appropriate word or phrase from among candidate near-synonyms or paraphrases is a significant language generation problem since even though near-synonyms and paraphrases are close in meaning, they differ in connotation and denotation in ways that may be significant to the desired effect of the generation output: for example, word choice can change a sentence from advice to admonish- ment. Particular applications that have been cited as having a use for modules which make effective word and phrase choices among closely related options are summarisation and rewriting (Barzilay and Lee, 2003). Inkpen and Hirst (2006) extended the genera- tion system HALogen (Langkilde and Knight, 1998; Langkilde, 2000) to include such a module. We discuss a particular aspect of choice between closely related words and phrases: choice between words when there is any difference in meaning or at- titude. Typical examples are frugal and stingy; slen- der and skinny; and error and blunder. In this paper, as in Gardiner and Dras (2007), we explore whether corpus statistics methods have promise in discriminating between near-synonyms with attitude differences, particularly compared to near-synonyms that do not differ in attitude. In our work, we used the work of (Edmonds, 1997), the first to attempt to distinguish among near-synonyms, adopting a corpus statistics approach. Based on that work, we found that there was a significant difference in attitudinal versus non-attitudinal near- synonyms. However, the Edmonds algorithm pro- duced on the whole poor results, only a little …

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