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        <title>Robust Word Vectors: Context-Informed Embeddings for Noisy Texts</title>
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        <namePart type="given">Valentin</namePart>
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        <namePart type="family">Logacheva</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>We suggest a new language-independent architecture of robust word vectors (RoVe). It is designed to alleviate the issue of typos, which are common in almost any user-generated content, and hinder automatic text processing. Our model is morphologically motivated, which allows it to deal with unseen word forms in morphologically rich languages. We present the results on a number of Natural Language Processing (NLP) tasks and languages for the variety of related architectures and show that proposed architecture is typo-proof.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6108</identifier>
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%0 Conference Proceedings
%T Robust Word Vectors: Context-Informed Embeddings for Noisy Texts
%A Malykh, Valentin
%A Logacheva, Varvara
%A Khakhulin, Taras
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F malykh-etal-2018-robust
%X We suggest a new language-independent architecture of robust word vectors (RoVe). It is designed to alleviate the issue of typos, which are common in almost any user-generated content, and hinder automatic text processing. Our model is morphologically motivated, which allows it to deal with unseen word forms in morphologically rich languages. We present the results on a number of Natural Language Processing (NLP) tasks and languages for the variety of related architectures and show that proposed architecture is typo-proof.
%R 10.18653/v1/W18-6108
%U https://aclanthology.org/W18-6108
%U https://doi.org/10.18653/v1/W18-6108
%P 54-63

Markdown (Informal)

[Robust Word Vectors: Context-Informed Embeddings for Noisy Texts](https://aclanthology.org/W18-6108) (Malykh et al., WNUT 2018)

• Robust Word Vectors: Context-Informed Embeddings for Noisy Texts (Malykh et al., WNUT 2018)

ACL

• Valentin Malykh, Varvara Logacheva, and Taras Khakhulin. 2018. Robust Word Vectors: Context-Informed Embeddings for Noisy Texts. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 54–63, Brussels, Belgium. Association for Computational Linguistics.