@inproceedings{yaghoobzadeh-schutze-2018-multi,
    title = "Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing",
    author = {Yaghoobzadeh, Yadollah  and
      Sch{\"u}tze, Hinrich},
    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/add-emnlp-2024-awards/D18-1343/",
    doi = "10.18653/v1/D18-1343",
    pages = "3060--3066",
    abstract = "Accurate and complete knowledge bases (KBs) are paramount in NLP. We employ mul-itiview learning for increasing the accuracy and coverage of entity type information in KBs. We rely on two metaviews: language and representation. For language, we consider high-resource and low-resource languages from Wikipedia. For representation, we consider representations based on the context distribution of the entity (i.e., on its embedding), on the entity`s name (i.e., on its surface form) and on its description in Wikipedia. The two metaviews language and representation can be freely combined: each pair of language and representation (e.g., German embedding, English description, Spanish name) is a distinct view. Our experiments on entity typing with fine-grained classes demonstrate the effectiveness of multiview learning. We release MVET, a large multiview {---} and, in particular, multilingual {---} entity typing dataset we created. Mono- and multilingual fine-grained entity typing systems can be evaluated on this dataset."
}