WordNet-based similarity metrics for adjectives

Emiel van Miltenburg


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.
Anthology ID:
2016.gwc-1.58
Volume:
Proceedings of the 8th Global WordNet Conference (GWC)
Month:
27--30 January
Year:
2016
Address:
Bucharest, Romania
Venue:
GWC
SIG:
Publisher:
Global Wordnet Association
Note:
Pages:
419–423
Language:
URL:
https://aclanthology.org/2016.gwc-1.58
DOI:
Bibkey:
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PDF:
https://aclanthology.org/2016.gwc-1.58.pdf