@inproceedings{khodak-etal-2017-automated,
    title = "Automated {W}ord{N}et Construction Using Word Embeddings",
    author = "Khodak, Mikhail  and
      Risteski, Andrej  and
      Fellbaum, Christiane  and
      Arora, Sanjeev",
    editor = "Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-1902",
    doi = "10.18653/v1/W17-1902",
    pages = "12--23",
    abstract = "We present a fully unsupervised method for automated construction of WordNets based upon recent advances in distributional representations of sentences and word-senses combined with readily available machine translation tools. The approach requires very few linguistic resources and is thus extensible to multiple target languages. To evaluate our method we construct two 600-word testsets for word-to-synset matching in French and Russian using native speakers and evaluate the performance of our method along with several other recent approaches. Our method exceeds the best language-specific and multi-lingual automated WordNets in F-score for both languages. The databases we construct for French and Russian, both languages without large publicly available manually constructed WordNets, will be publicly released along with the testsets.",
}