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        <title>Personality Trait Identification Using the Russian Feature Extraction Toolkit</title>
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        <namePart type="given">James</namePart>
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        <namePart type="given">Valerie</namePart>
        <namePart type="family">Novak</namePart>
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        <namePart type="given">Victor</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>Feature engineering is an important step in classical NLP pipelines, but machine learning engineers may not be aware of the signals to look for when processing foreign language text. The Russian Feature Extraction Toolkit (RFET) is a collection of feature extraction libraries bundled for ease of use by engineers who do not speak Russian. RFET’s current feature set includes features applicable to social media genres of text and to computational social science tasks. We demonstrate the effectiveness of the tool by using it in a personality trait identification task. We compare the performance of Support Vector Machines (SVMs) trained with and without the features provided by RFET; we also compare it to a SVM with neural embedding features generated by Sentence-BERT.</abstract>
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%0 Conference Proceedings
%T Personality Trait Identification Using the Russian Feature Extraction Toolkit
%A Hull, James R.
%A Novak, Valerie
%A Rytting, C. Anton
%A Rodrigues, Paul
%A Frank, Victor M.
%A Swahn, Matthew
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F hull-etal-2021-personality
%X Feature engineering is an important step in classical NLP pipelines, but machine learning engineers may not be aware of the signals to look for when processing foreign language text. The Russian Feature Extraction Toolkit (RFET) is a collection of feature extraction libraries bundled for ease of use by engineers who do not speak Russian. RFET’s current feature set includes features applicable to social media genres of text and to computational social science tasks. We demonstrate the effectiveness of the tool by using it in a personality trait identification task. We compare the performance of Support Vector Machines (SVMs) trained with and without the features provided by RFET; we also compare it to a SVM with neural embedding features generated by Sentence-BERT.
%U https://aclanthology.org/2021.ranlp-1.66
%P 583-592

Markdown (Informal)

[Personality Trait Identification Using the Russian Feature Extraction Toolkit](https://aclanthology.org/2021.ranlp-1.66) (Hull et al., RANLP 2021)

• Personality Trait Identification Using the Russian Feature Extraction Toolkit (Hull et al., RANLP 2021)

ACL

• James R. Hull, Valerie Novak, C. Anton Rytting, Paul Rodrigues, Victor M. Frank, and Matthew Swahn. 2021. Personality Trait Identification Using the Russian Feature Extraction Toolkit. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 583–592, Held Online. INCOMA Ltd..