Wordnet-based similarity metrics for adjectives

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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 languageEnglish
Title of host publicationProceedings of the 8th Global WordNet Conference
Place of PublicationBucharest, Romania
PublisherGlobal WordNet Association
ISBN (Electronic)9789730207286
Publication statusPublished - 2016
Externally publishedYes


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