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    <titleInfo>
        <title>Classifying Referential and Non-referential It Using Gaze</title>
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        <namePart type="given">Victoria</namePart>
        <namePart type="family">Yaneva</namePart>
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    <name type="personal">
        <namePart type="given">Ruslan</namePart>
        <namePart type="family">Mitkov</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones. In this paper we use eye-tracking data to learn how humans perform this disambiguation and use this knowledge to improve the automatic classification of it. We show that by using gaze data and a POS-tagger we are able to significantly outperform a common baseline and classify between three categories of it with an accuracy comparable to that of linguistic-based approaches. In addition, the discriminatory power of specific gaze features informs the way humans process the pronoun, which, to the best of our knowledge, has not been explored using data from a natural reading task.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1528</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Classifying Referential and Non-referential It Using Gaze
%A Yaneva, Victoria
%A Ha, Le An
%A Evans, Richard
%A Mitkov, Ruslan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F yaneva-etal-2018-classifying
%X When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones. In this paper we use eye-tracking data to learn how humans perform this disambiguation and use this knowledge to improve the automatic classification of it. We show that by using gaze data and a POS-tagger we are able to significantly outperform a common baseline and classify between three categories of it with an accuracy comparable to that of linguistic-based approaches. In addition, the discriminatory power of specific gaze features informs the way humans process the pronoun, which, to the best of our knowledge, has not been explored using data from a natural reading task.
%R 10.18653/v1/D18-1528
%U https://aclanthology.org/D18-1528
%U https://doi.org/10.18653/v1/D18-1528
%P 4896-4901

Markdown (Informal)

[Classifying Referential and Non-referential It Using Gaze](https://aclanthology.org/D18-1528) (Yaneva et al., EMNLP 2018)

• Classifying Referential and Non-referential It Using Gaze (Yaneva et al., EMNLP 2018)

ACL

• Victoria Yaneva, Le An Ha, Richard Evans, and Ruslan Mitkov. 2018. Classifying Referential and Non-referential It Using Gaze. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4896–4901, Brussels, Belgium. Association for Computational Linguistics.