@inproceedings{ma-etal-2016-label,
    title = "Label Embedding for Zero-shot Fine-grained Named Entity Typing",
    author = "Ma, Yukun  and
      Cambria, Erik  and
      Gao, Sa",
    editor = "Matsumoto, Yuji  and
      Prasad, Rashmi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/fix-sig-urls/C16-1017/",
    pages = "171--180",
    abstract = "Named entity typing is the task of detecting the types of a named entity in context. For instance, given ``Eric is giving a presentation'', our goal is to infer that `Eric' is a speaker or a presenter and a person. Existing approaches to named entity typing cannot work with a growing type set and fails to recognize entity mentions of unseen types. In this paper, we present a label embedding method that incorporates prototypical and hierarchical information to learn pre-trained label embeddings. In addition, we adapt a zero-shot learning framework that can predict both seen and previously unseen entity types. We perform evaluation on three benchmark datasets with two settings: 1) few-shots recognition where all types are covered by the training set; and 2) zero-shot recognition where fine-grained types are assumed absent from training set. Results show that prior knowledge encoded using our label embedding methods can significantly boost the performance of classification for both cases."
}