@inproceedings{saedi-etal-2018-wordnet,
    title = "{W}ord{N}et Embeddings",
    author = "Saedi, Chakaveh  and
      Branco, Ant{\'o}nio  and
      Ant{\'o}nio Rodrigues, Jo{\~a}o  and
      Silva, Jo{\~a}o",
    editor = "Augenstein, Isabelle  and
      Cao, Kris  and
      He, He  and
      Hill, Felix  and
      Gella, Spandana  and
      Kiros, Jamie  and
      Mei, Hongyuan  and
      Misra, Dipendra",
    booktitle = "Proceedings of the Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-3016/",
    doi = "10.18653/v1/W18-3016",
    pages = "122--131",
    abstract = "Semantic networks and semantic spaces have been two prominent approaches to represent lexical semantics. While a unified account of the lexical meaning relies on one being able to convert between these representations, in both directions, the conversion direction from semantic networks into semantic spaces started to attract more attention recently. In this paper we present a methodology for this conversion and assess it with a case study. When it is applied over WordNet, the performance of the resulting embeddings in a mainstream semantic similarity task is very good, substantially superior to the performance of word embeddings based on very large collections of texts like word2vec."
}