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        <title>Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition</title>
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    <name type="personal">
        <namePart type="given">Mehmet</namePart>
        <namePart type="given">Ali</namePart>
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    <name type="personal">
        <namePart type="given">Volkan</namePart>
        <namePart type="family">Cirik</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Deniz</namePart>
        <namePart type="family">Yuret</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Learning syntactic categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show that models based on probable substitutes learn more accurate categories with fewer examples compared to models based on frames.</abstract>
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            <start>707</start>
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%0 Conference Proceedings
%T Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition
%A Yatbaz, Mehmet Ali
%A Cirik, Volkan
%A Küntay, Aylin
%A Yuret, Deniz
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F yatbaz-etal-2016-learning
%X Learning syntactic categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show that models based on probable substitutes learn more accurate categories with fewer examples compared to models based on frames.
%U https://aclanthology.org/C16-1068
%P 707-716

Markdown (Informal)

[Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition](https://aclanthology.org/C16-1068) (Yatbaz et al., COLING 2016)

• Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition (Yatbaz et al., COLING 2016)

ACL

• Mehmet Ali Yatbaz, Volkan Cirik, Aylin Küntay, and Deniz Yuret. 2016. Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 707–716, Osaka, Japan. The COLING 2016 Organizing Committee.