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Bias Modeling for Distantly Supervised Relation Extraction

机译：偏远关系抽取中的偏差建模

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摘要

Distant supervision (DS) automatically annotates free text with relation mentions from existing knowledge bases (KBs), providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP). However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1) bias-dist to model the relative distance between points (instances) and classes (relation centers); (2) bias-reward to model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models: MIML-dist, MIML-distclassify, and MIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods.
机译：远程监管（DS）会自动用现有知识库（KB）中的关系提及对自由文本进行注释，从而缓解自然语言处理（NLP）中用于关系提取的训练数据不足的问题。然而，启发式注释过程不能保证所生成标签的正确性，从而引发了关于如何有效利用嘈杂训练数据的热门研究问题。在本文中，我们对两种类型的偏差进行建模以减少噪声：（1）偏差距离用于建模点（实例）与类（关系中心）之间的相对距离； （2）偏向奖励以模拟每个启发式生成的标签不正确的可能性。基于这些偏见，我们在最新的远程监督学习算法的基础上，提出了三种噪声容忍模型：MIML dist，MIML distclassify和MIML奖励。实验评估与KBP数据集上的三种界标方法相比，验证了所提出方法的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第19期|969053.1-969053.10|共10页
  • 作者

    Xiang Yang; Zhang Yaoyun; Wang Xiaolong; Qin Yang; Han Wenying;

  • 作者单位

    Harbin Inst Technol, Shenzhen Grad Sch, Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China.;

    Harbin Inst Technol, Shenzhen Grad Sch, Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China.;

    Harbin Inst Technol, Shenzhen Grad Sch, Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China.;

    Harbin Inst Technol, Shenzhen Grad Sch, Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China.;

    Harbin Inst Technol, Shenzhen Grad Sch, Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China.;

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