@inproceedings{zhang-wang-2017-noise,
    title = "Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric {B}ayesian Perspective",
    author = "Zhang, Qing  and
      Wang, Houfeng",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D17-1192/",
    doi = "10.18653/v1/D17-1192",
    pages = "1808--1813",
    abstract = "For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substantially higher top precision improvements over the traditional state-of-the-art approaches."
}