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        <title>Word Embeddings for Multi-label Document Classification</title>
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    <name type="personal">
        <namePart type="given">Ladislav</namePart>
        <namePart type="family">Lenc</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>In this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label classification scenario. The embeddings are used in three convolutional neural network topologies. The experiments are realized on the Czech ČTK and English Reuters-21578 standard corpora. We compare the results of word2vec static and trainable embeddings with randomly initialized word vectors. We conclude that initialization does not play an important role for classification. However, learning of word vectors is crucial to obtain good results.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_057</identifier>
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%0 Conference Proceedings
%T Word Embeddings for Multi-label Document Classification
%A Lenc, Ladislav
%A Král, Pavel
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F lenc-kral-2017-word
%X In this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label classification scenario. The embeddings are used in three convolutional neural network topologies. The experiments are realized on the Czech ČTK and English Reuters-21578 standard corpora. We compare the results of word2vec static and trainable embeddings with randomly initialized word vectors. We conclude that initialization does not play an important role for classification. However, learning of word vectors is crucial to obtain good results.
%R 10.26615/978-954-452-049-6_057
%U https://doi.org/10.26615/978-954-452-049-6_057
%P 431-437

Markdown (Informal)

[Word Embeddings for Multi-label Document Classification](https://doi.org/10.26615/978-954-452-049-6_057) (Lenc & Král, RANLP 2017)

• Word Embeddings for Multi-label Document Classification (Lenc & Král, RANLP 2017)

ACL

• Ladislav Lenc and Pavel Král. 2017. Word Embeddings for Multi-label Document Classification. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 431–437, Varna, Bulgaria. INCOMA Ltd..