@inproceedings{daix-moreux-galle-2019-joint,
    title = "Joint Semantic and Distributional Word Representations with Multi-Graph Embeddings",
    author = "Daix-Moreux, Pierre  and
      Gall{\'e}, Matthias",
    editor = "Ustalov, Dmitry  and
      Somasundaran, Swapna  and
      Jansen, Peter  and
      Glava{\v{s}}, Goran  and
      Riedl, Martin  and
      Surdeanu, Mihai  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/D19-5314/",
    doi = "10.18653/v1/D19-5314",
    pages = "118--123",
    abstract = "Word embeddings continue to be of great use for NLP researchers and practitioners due to their training speed and easiness of use and distribution. Prior work has shown that the representation of those words can be improved by the use of semantic knowledge-bases. In this paper we propose a novel way of combining those knowledge-bases while the lexical information of co-occurrences of words remains. It is conceptually clear, as it consists in mapping both distributional and semantic information into a multi-graph and modifying existing node embeddings techniques to compute word representations. Our experiments show improved results compared to vanilla word embeddings, retrofitting and concatenation techniques using the same information, on a variety of data-sets of word similarities."
}