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        <title>Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings</title>
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        <namePart type="given">Simon</namePart>
        <namePart type="family">Baker</namePart>
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    <name type="personal">
        <namePart type="given">Douwe</namePart>
        <namePart type="family">Kiela</namePart>
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    <name type="personal">
        <namePart type="given">Anna</namePart>
        <namePart type="family">Korhonen</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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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>The conventional solution for handling sparsely labelled data is extensive feature engineering. This is time consuming and task and domain specific. We present a novel approach for learning embedded features that aims to alleviate this problem. Our approach jointly learns embeddings at different levels of granularity (word, sentence and document) along with the class labels. The intuition is that topic semantics represented by embeddings at multiple levels results in better classification. We evaluate this approach in unsupervised and semi-supervised settings on two sparsely labelled classification tasks, outperforming the handcrafted models and several embedding baselines.</abstract>
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%0 Conference Proceedings
%T Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings
%A Baker, Simon
%A Kiela, Douwe
%A Korhonen, Anna
%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 baker-etal-2016-robust
%X The conventional solution for handling sparsely labelled data is extensive feature engineering. This is time consuming and task and domain specific. We present a novel approach for learning embedded features that aims to alleviate this problem. Our approach jointly learns embeddings at different levels of granularity (word, sentence and document) along with the class labels. The intuition is that topic semantics represented by embeddings at multiple levels results in better classification. We evaluate this approach in unsupervised and semi-supervised settings on two sparsely labelled classification tasks, outperforming the handcrafted models and several embedding baselines.
%U https://aclanthology.org/C16-1220
%P 2333-2343

Markdown (Informal)

[Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings](https://aclanthology.org/C16-1220) (Baker et al., COLING 2016)

• Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings (Baker et al., COLING 2016)

ACL

• Simon Baker, Douwe Kiela, and Anna Korhonen. 2016. Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2333–2343, Osaka, Japan. The COLING 2016 Organizing Committee.