Exploring Approaches to Discriminating among Near-Synonyms

Near-synonyms are words that mean approximately the same thing, and which tend to be assigned to the same leaf in ontologies such as WordNet. However, they can differ from each other subtly in both meaning and usage—consider the pair of nearsynonyms 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 hypothesis that some kind of corpus statistics approach may still be effective in some situations, particularly if the near-synonyms differ 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 nearsynonym may be particularly amenable to corpus statistics methods. In this paper we investigate whether this result extends to a different corpus statistics method and in addition we analyse the results with respect to a possible confounding factor discussed in the previous work: the skewness of the sets of near synonyms. Our results show that the relationship between success in prediction and the nature of the near-synonyms is method dependent and that skewness is a more significant factor.