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        <title>Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective</title>
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    <name type="personal">
        <namePart type="given">Qing</namePart>
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            <namePart type="given">Martha</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1192</identifier>
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%0 Conference Proceedings
%T Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective
%A Zhang, Qing
%A Wang, Houfeng
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zhang-wang-2017-noise
%X 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.
%R 10.18653/v1/D17-1192
%U https://aclanthology.org/D17-1192/
%U https://doi.org/10.18653/v1/D17-1192
%P 1808-1813

Markdown (Informal)

[Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective](https://aclanthology.org/D17-1192/) (Zhang & Wang, EMNLP 2017)

• Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective (Zhang & Wang, EMNLP 2017)

ACL

• Qing Zhang and Houfeng Wang. 2017. Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1808–1813, Copenhagen, Denmark. Association for Computational Linguistics.