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    <titleInfo>
        <title>Classifying Author Intention for Writer Feedback in Related Work</title>
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    <name type="personal">
        <namePart type="given">Arlene</namePart>
        <namePart type="family">Casey</namePart>
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    <name type="personal">
        <namePart type="given">Bonnie</namePart>
        <namePart type="family">Webber</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Dorota</namePart>
        <namePart type="family">Glowacka</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>The ability to produce high-quality publishable material is critical to academic success but many Post-Graduate students struggle to learn to do so. While recent years have seen an increase in tools designed to provide feedback on aspects of writing, one aspect that has so far been neglected is the Related Work section of academic research papers. To address this, we have trained a supervised classifier on a corpus of 94 Related Work sections and evaluated it against a manually annotated gold standard. The classifier uses novel features pertaining to citation types and co-reference, along with patterns found from studying Related Works. We show that these novel features contribute to classifier performance with performance being favourable compared to other similar works that classify author intentions and consider feedback for academic writing.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_021</identifier>
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        <url>https://aclanthology.org/R19-1021</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Classifying Author Intention for Writer Feedback in Related Work
%A Casey, Arlene
%A Webber, Bonnie
%A Glowacka, Dorota
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F casey-etal-2019-classifying
%X The ability to produce high-quality publishable material is critical to academic success but many Post-Graduate students struggle to learn to do so. While recent years have seen an increase in tools designed to provide feedback on aspects of writing, one aspect that has so far been neglected is the Related Work section of academic research papers. To address this, we have trained a supervised classifier on a corpus of 94 Related Work sections and evaluated it against a manually annotated gold standard. The classifier uses novel features pertaining to citation types and co-reference, along with patterns found from studying Related Works. We show that these novel features contribute to classifier performance with performance being favourable compared to other similar works that classify author intentions and consider feedback for academic writing.
%R 10.26615/978-954-452-056-4_021
%U https://aclanthology.org/R19-1021
%U https://doi.org/10.26615/978-954-452-056-4_021
%P 178-187

Markdown (Informal)

[Classifying Author Intention for Writer Feedback in Related Work](https://aclanthology.org/R19-1021) (Casey et al., RANLP 2019)

• Classifying Author Intention for Writer Feedback in Related Work (Casey et al., RANLP 2019)

ACL

• Arlene Casey, Bonnie Webber, and Dorota Glowacka. 2019. Classifying Author Intention for Writer Feedback in Related Work. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 178–187, Varna, Bulgaria. INCOMA Ltd..