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        <title>Named Entity Recognition without Labelled Data: A Weak Supervision Approach</title>
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        <namePart type="given">Aliaksandr</namePart>
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        <namePart type="given">Samia</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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            <namePart type="given">Joel</namePart>
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    <abstract>Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions. A sequence labelling model can finally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model.</abstract>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T Named Entity Recognition without Labelled Data: A Weak Supervision Approach
%A Lison, Pierre
%A Barnes, Jeremy
%A Hubin, Aliaksandr
%A Touileb, Samia
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lison-etal-2020-named
%X Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions. A sequence labelling model can finally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model.
%R 10.18653/v1/2020.acl-main.139
%U https://aclanthology.org/2020.acl-main.139
%U https://doi.org/10.18653/v1/2020.acl-main.139
%P 1518-1533

Markdown (Informal)

[Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://aclanthology.org/2020.acl-main.139) (Lison et al., ACL 2020)

• Named Entity Recognition without Labelled Data: A Weak Supervision Approach (Lison et al., ACL 2020)

ACL

• Pierre Lison, Jeremy Barnes, Aliaksandr Hubin, and Samia Touileb. 2020. Named Entity Recognition without Labelled Data: A Weak Supervision Approach. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1518–1533, Online. Association for Computational Linguistics.