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        <title>Named Entity Recognition with Partially Annotated Training Data</title>
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        <namePart type="given">Stephen</namePart>
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    <name type="personal">
        <namePart type="given">Snigdha</namePart>
        <namePart type="family">Chaturvedi</namePart>
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    <name type="personal">
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        <dateIssued>2019-nov</dateIssued>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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    <abstract>Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated. We study the problem of Named Entity Recognition (NER) with partially annotated training data in which a fraction of the named entities are labeled, and all other tokens, entities or otherwise, are labeled as non-entity by default. In order to train on this noisy dataset, we need to distinguish between the true and false negatives. To this end, we introduce a constraint-driven iterative algorithm that learns to detect false negatives in the noisy set and downweigh them, resulting in a weighted training set. With this set, we train a weighted NER model. We evaluate our algorithm with weighted variants of neural and non-neural NER models on data in 8 languages from several language and script families, showing strong ability to learn from partial data. Finally, to show real-world efficacy, we evaluate on a Bengali NER corpus annotated by non-speakers, outperforming the prior state-of-the-art by over 5 points F1.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1060</identifier>
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%0 Conference Proceedings
%T Named Entity Recognition with Partially Annotated Training Data
%A Mayhew, Stephen
%A Chaturvedi, Snigdha
%A Tsai, Chen-Tse
%A Roth, Dan
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F mayhew-etal-2019-named
%X Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated. We study the problem of Named Entity Recognition (NER) with partially annotated training data in which a fraction of the named entities are labeled, and all other tokens, entities or otherwise, are labeled as non-entity by default. In order to train on this noisy dataset, we need to distinguish between the true and false negatives. To this end, we introduce a constraint-driven iterative algorithm that learns to detect false negatives in the noisy set and downweigh them, resulting in a weighted training set. With this set, we train a weighted NER model. We evaluate our algorithm with weighted variants of neural and non-neural NER models on data in 8 languages from several language and script families, showing strong ability to learn from partial data. Finally, to show real-world efficacy, we evaluate on a Bengali NER corpus annotated by non-speakers, outperforming the prior state-of-the-art by over 5 points F1.
%R 10.18653/v1/K19-1060
%U https://aclanthology.org/K19-1060
%U https://doi.org/10.18653/v1/K19-1060
%P 645-655

Markdown (Informal)

[Named Entity Recognition with Partially Annotated Training Data](https://aclanthology.org/K19-1060) (Mayhew et al., CoNLL 2019)

• Named Entity Recognition with Partially Annotated Training Data (Mayhew et al., CoNLL 2019)

ACL

• Stephen Mayhew, Snigdha Chaturvedi, Chen-Tse Tsai, and Dan Roth. 2019. Named Entity Recognition with Partially Annotated Training Data. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 645–655, Hong Kong, China. Association for Computational Linguistics.