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        <title>Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces</title>
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    <name type="personal">
        <namePart type="given">Anthony</namePart>
        <namePart type="family">Rios</namePart>
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    <name type="personal">
        <namePart type="given">Ramakanth</namePart>
        <namePart type="family">Kavuluru</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2% and 4.8% in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3% and 19%.</abstract>
    <identifier type="citekey">rios-kavuluru-2018-shot</identifier>
    <identifier type="doi">10.18653/v1/D18-1352</identifier>
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        <url>https://aclanthology.org/D18-1352/</url>
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        <date>2018-oct-nov</date>
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            <start>3132</start>
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%0 Conference Proceedings
%T Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces
%A Rios, Anthony
%A Kavuluru, Ramakanth
%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 rios-kavuluru-2018-shot
%X Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2% and 4.8% in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3% and 19%.
%R 10.18653/v1/D18-1352
%U https://aclanthology.org/D18-1352/
%U https://doi.org/10.18653/v1/D18-1352
%P 3132-3142

Markdown (Informal)

[Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces](https://aclanthology.org/D18-1352/) (Rios & Kavuluru, EMNLP 2018)

• Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces (Rios & Kavuluru, EMNLP 2018)

ACL

• Anthony Rios and Ramakanth Kavuluru. 2018. Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3132–3142, Brussels, Belgium. Association for Computational Linguistics.