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        <title>To normalize, or not to normalize: The impact of normalization on Part-of-Speech tagging</title>
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        <namePart type="given">Rob</namePart>
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        <namePart type="given">Malvina</namePart>
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            <title>Proceedings of the 3rd Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Wei</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>Does normalization help Part-of-Speech (POS) tagging accuracy on noisy, non-canonical data? To the best of our knowledge, little is known on the actual impact of normalization in a real-world scenario, where gold error detection is not available. We investigate the effect of automatic normalization on POS tagging of tweets. We also compare normalization to strategies that leverage large amounts of unlabeled data kept in its raw form. Our results show that normalization helps, but does not add consistently beyond just word embedding layer initialization. The latter approach yields a tagging model that is competitive with a Twitter state-of-the-art tagger.</abstract>
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    <identifier type="doi">10.18653/v1/W17-4404</identifier>
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        <date>2017-09</date>
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            <start>31</start>
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%0 Conference Proceedings
%T To normalize, or not to normalize: The impact of normalization on Part-of-Speech tagging
%A van der Goot, Rob
%A Plank, Barbara
%A Nissim, Malvina
%Y Derczynski, Leon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%S Proceedings of the 3rd Workshop on Noisy User-generated Text
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F van-der-goot-etal-2017-normalize
%X Does normalization help Part-of-Speech (POS) tagging accuracy on noisy, non-canonical data? To the best of our knowledge, little is known on the actual impact of normalization in a real-world scenario, where gold error detection is not available. We investigate the effect of automatic normalization on POS tagging of tweets. We also compare normalization to strategies that leverage large amounts of unlabeled data kept in its raw form. Our results show that normalization helps, but does not add consistently beyond just word embedding layer initialization. The latter approach yields a tagging model that is competitive with a Twitter state-of-the-art tagger.
%R 10.18653/v1/W17-4404
%U https://aclanthology.org/W17-4404
%U https://doi.org/10.18653/v1/W17-4404
%P 31-39

Markdown (Informal)

[To normalize, or not to normalize: The impact of normalization on Part-of-Speech tagging](https://aclanthology.org/W17-4404) (van der Goot et al., WNUT 2017)

• To normalize, or not to normalize: The impact of normalization on Part-of-Speech tagging (van der Goot et al., WNUT 2017)

ACL

• Rob van der Goot, Barbara Plank, and Malvina Nissim. 2017. To normalize, or not to normalize: The impact of normalization on Part-of-Speech tagging. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 31–39, Copenhagen, Denmark. Association for Computational Linguistics.