@inproceedings{tielin-etal-2021-ns,
    title = "{NS}-Hunter: {BERT}-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification",
    author = "Tielin, Shen  and
      Daling, Wang  and
      Shi, Feng  and
      Yifei, Zhang",
    editor = "Li, Sheng  and
      Sun, Maosong  and
      Liu, Yang  and
      Wu, Hua  and
      Liu, Kang  and
      Che, Wanxiang  and
      He, Shizhu  and
      Rao, Gaoqi",
    booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2021",
    address = "Huhhot, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.ccl-1.99/",
    pages = "1109--1120",
    language = "eng",
    abstract = "Distant supervision can generate large-scale relation classification data quickly and economi-cally. However a great number of noise sentences are introduced which can not express their labeled relations. By means of pre-trained language model BERT{'}s powerful function in this paper we propose a BERT-based semantic denoising approach for distantly supervised relation classification. In detail we define an entity pair as a source entity and a target entity. For the specific sentences whose target entities in BERT-vocabulary (one-token word) we present the differences of dependency between two entities for noise and non-noise sentences. For general sentences whose target entity is multi-token word we further present the differences of last hid-den states of [MASK]-entity (MASK-lhs for short) in BERT for noise and non-noise sentences. We regard the dependency and MASK-lhs in BERT as two semantic features of sentences. With BERT we capture the dependency feature to discriminate specific sentences first then capturethe MASK-lhs feature to denoise distant supervision datasets. We propose NS-Hunter a noveldenoising model which leverages BERT-cloze ability to capture the two semantic features andintegrates above functions. According to the experiment on NYT data our NS-Hunter modelachieves the best results in distant supervision denoising and sentence-level relation classification. Keywords: Distant supervision relation classification semantic denoisingIntroduction"
}