@inproceedings{zhang-etal-2018-fine,
    title = "Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds",
    author = "Zhang, Sheng  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    editor = "Nissim, Malvina  and
      Berant, Jonathan  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/S18-2022/",
    doi = "10.18653/v1/S18-2022",
    pages = "173--179",
    abstract = "Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context {--} both document and sentence level information {--} than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets."
}