@inproceedings{kanada-etal-2017-classifying,
    title = "Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations",
    author = "Kanada, Kentaro  and
      Kobayashi, Tetsunori  and
      Hayashi, Yoshihiko",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/W17-1905/",
    doi = "10.18653/v1/W17-1905",
    pages = "37--46",
    abstract = "This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by {\textquotedblleft}sense representations{\textquotedblright} (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations."
}