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        <title>Initializing neural networks for hierarchical multi-label text classification</title>
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            <namePart type="given">Kevin</namePart>
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            <namePart type="family">Cohen</namePart>
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        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
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        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
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        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use a one-vs.-rest (OVR) classification setup, where a binary classifier is trained for each label in the taxonomy or ontology where all instances not belonging to the class are considered negative examples. The main drawbacks to this approach are that dependencies between classes are not leveraged in the training and classification process, and the additional computational cost of training parallel classifiers. In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model final hidden layer such that it leverages label co-occurrence relations such as hypernymy. This approach elegantly lends itself to hierarchical classification. We evaluated this approach using two hierarchical multi-label text classification tasks in the biomedical domain using both sentence- and document-level classification. Our evaluation shows promising results for this approach.</abstract>
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%0 Conference Proceedings
%T Initializing neural networks for hierarchical multi-label text classification
%A Baker, Simon
%A Korhonen, Anna
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F baker-korhonen-2017-initializing
%X Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use a one-vs.-rest (OVR) classification setup, where a binary classifier is trained for each label in the taxonomy or ontology where all instances not belonging to the class are considered negative examples. The main drawbacks to this approach are that dependencies between classes are not leveraged in the training and classification process, and the additional computational cost of training parallel classifiers. In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model final hidden layer such that it leverages label co-occurrence relations such as hypernymy. This approach elegantly lends itself to hierarchical classification. We evaluated this approach using two hierarchical multi-label text classification tasks in the biomedical domain using both sentence- and document-level classification. Our evaluation shows promising results for this approach.
%R 10.18653/v1/W17-2339
%U https://aclanthology.org/W17-2339
%U https://doi.org/10.18653/v1/W17-2339
%P 307-315

Markdown (Informal)

[Initializing neural networks for hierarchical multi-label text classification](https://aclanthology.org/W17-2339) (Baker & Korhonen, BioNLP 2017)

• Initializing neural networks for hierarchical multi-label text classification (Baker & Korhonen, BioNLP 2017)

ACL

• Simon Baker and Anna Korhonen. 2017. Initializing neural networks for hierarchical multi-label text classification. In BioNLP 2017, pages 307–315, Vancouver, Canada,. Association for Computational Linguistics.