@inproceedings{van-miltenburg-2016-wordnet,
    title = "{W}ord{N}et-based similarity metrics for adjectives",
    author = "van Miltenburg, Emiel",
    editor = "Fellbaum, Christiane  and
      Vossen, Piek  and
      Mititelu, Verginica Barbu  and
      Forascu, Corina",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://preview.aclanthology.org/moar-dois/2016.gwc-1.58/",
    pages = "419--423",
    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."
}