@inproceedings{chen-etal-2018-word,
    title = "Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings",
    author = "Chen, Hong-You  and
      Lee, Cheng-Syuan  and
      Liao, Keng-Te  and
      Lin, Shou-De",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/D18-1519/",
    doi = "10.18653/v1/D18-1519",
    pages = "4834--4839",
    abstract = "Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly."
}