@inproceedings{zhang-etal-2018-sjtu,
    title = "{SJTU}-{NLP} at {S}em{E}val-2018 Task 9: Neural Hypernym Discovery with Term Embeddings",
    author = "Zhang, Zhuosheng  and
      Li, Jiangtong  and
      Zhao, Hai  and
      Tang, Bingjie",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S18-1147/",
    doi = "10.18653/v1/S18-1147",
    pages = "903--908",
    abstract = "This paper describes a hypernym discovery system for our participation in the SemEval-2018 Task 9, which aims to discover the best (set of) candidate hypernyms for input concepts or entities, given the search space of a pre-defined vocabulary. We introduce a neural network architecture for the concerned task and empirically study various neural network models to build the representations in latent space for words and phrases. The evaluated models include convolutional neural network, long-short term memory network, gated recurrent unit and recurrent convolutional neural network. We also explore different embedding methods, including word embedding and sense embedding for better performance."
}