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        <title>NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification</title>
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        <namePart type="given">Shen</namePart>
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        <namePart type="family">Daling</namePart>
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    <name type="personal">
        <namePart type="given">Feng</namePart>
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    <name type="personal">
        <namePart type="given">Zhang</namePart>
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            <title>Proceedings of the 20th Chinese National Conference on Computational Linguistics</title>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <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”</abstract>
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        <url>https://aclanthology.org/2021.ccl-1.99</url>
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        <date>2021-aug</date>
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            <start>1109</start>
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%0 Conference Proceedings
%T NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification
%A Tielin, Shen
%A Daling, Wang
%A Shi, Feng
%A Yifei, Zhang
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 aug
%I Chinese Information Processing Society of China
%C Huhhot, China
%G English
%F tielin-etal-2021-ns
%X “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”
%U https://aclanthology.org/2021.ccl-1.99
%P 1109-1120

Markdown (Informal)

[NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification](https://aclanthology.org/2021.ccl-1.99) (Tielin et al., CCL 2021)

• NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification (Tielin et al., CCL 2021)

ACL

• Shen Tielin, Wang Daling, Feng Shi, and Zhang Yifei. 2021. NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 1109–1120, Huhhot, China. Chinese Information Processing Society of China.