Abstract
Le and Fokkens (2015) recently showed that taxonomy-based approaches are more reliable than corpus-based approaches in estimating human similarity ratings. On the other hand, distributional models provide much better coverage. The lack of an established similarity metric for adjectives in WordNet is a case in point. I present initial work to establish such a metric, and propose ways to move forward by looking at extensions to WordNet. I show that the shortest path distance between derivationally related forms provides a reliable estimate of adjective similarity. Furthermore, I find that a hybrid method combining this measure with vector-based similarity estimations gives us the best of both worlds: more reliable similarity estimations than vectors alone, but with the same coverage as corpus-based methods.
Original language | English |
---|---|
Title of host publication | Proceedings of the 8th Global WordNet Conference |
Place of Publication | Bucharest, Romania |
Publisher | Global WordNet Association |
Pages | 414-418 |
ISBN (Electronic) | 9789730207286 |
Publication status | Published - 2016 |
Externally published | Yes |