Computing Lexical Contrast
Table 8: Distributional similarity of word pairs. The measure proposed in Lin (1998) was used.
['[EMPTY]', 'average distributional similarity', 'standard deviation']
We used the same sets of high-contrast pairs, synonyms, and random pairs described in the previous sub-section to gather empirical proof of the distributional hypothesis. We calculated the distributional similarity between each pair in the three sets using Lin’s \shortciteLin98B measure. Observe that the high-contrast set has a much higher average distributional similarity than the random pairs control set, and interestingly it is also higher than the synonyms set. Once again, the high-contrast set has a large standard deviation. A two-sample t-test revealed that the high-contrast set is significantly different from both the random set and the synonyms set with a confidence interval of 0.05. This demonstrates that relative to other word pairs, high-contrast pairs tend to occur in similar contexts. We also find that the synonyms set has a significantly higher distributional similarity than the random pairs set (p<0.05). This shows that near-synonymous word pairs also occur in similar contexts (the distributional hypothesis of similarity). Further, a consequence of the large standard deviations in the cases of both high-contrast pairs and synonyms means that distributional similarity alone is not sufficient to determine whether two words are contrasting or synonymous. An automatic method for recognizing contrast will require additional cues. Our method uses PMI and other sources of information described in the next section.