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        <title>Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning</title>
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    <name type="personal">
        <namePart type="given">Minlong</namePart>
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        <namePart type="given">Xiaoyu</namePart>
        <namePart type="family">Xing</namePart>
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    <name type="personal">
        <namePart type="given">Qi</namePart>
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        <namePart type="given">Jinlan</namePart>
        <namePart type="family">Fu</namePart>
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    <name type="personal">
        <namePart type="given">Xuanjing</namePart>
        <namePart type="family">Huang</namePart>
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    <abstract>In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of the words constituting an entity. This greatly reduces the requirement on the quality of the dictionaries and makes our method generalize well with quite simple dictionaries. Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. We have published the source code at https://github.com/v-mipeng/LexiconNER.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1231</identifier>
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%0 Conference Proceedings
%T Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
%A Peng, Minlong
%A Xing, Xiaoyu
%A Zhang, Qi
%A Fu, Jinlan
%A Huang, Xuanjing
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F peng-etal-2019-distantly
%X In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of the words constituting an entity. This greatly reduces the requirement on the quality of the dictionaries and makes our method generalize well with quite simple dictionaries. Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. We have published the source code at https://github.com/v-mipeng/LexiconNER.
%R 10.18653/v1/P19-1231
%U https://aclanthology.org/P19-1231
%U https://doi.org/10.18653/v1/P19-1231
%P 2409-2419

Markdown (Informal)

[Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning](https://aclanthology.org/P19-1231) (Peng et al., ACL 2019)

• Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning (Peng et al., ACL 2019)

ACL

• Minlong Peng, Xiaoyu Xing, Qi Zhang, Jinlan Fu, and Xuanjing Huang. 2019. Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2409–2419, Florence, Italy. Association for Computational Linguistics.