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        <title>Word Sense Disambiguation with Recurrent Neural Networks</title>
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            <namePart type="given">Venelin</namePart>
            <namePart type="family">Kovatchev</namePart>
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        <name type="personal">
            <namePart type="given">Irina</namePart>
            <namePart type="family">Temnikova</namePart>
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        <name type="personal">
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        <name type="personal">
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            <namePart type="family">Nikolova</namePart>
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    <abstract>This paper presents a neural network architecture for word sense disambiguation (WSD). The architecture employs recurrent neural layers and more specifically LSTM cells, in order to capture information about word order and to easily incorporate distributed word representations (embeddings) as features, without having to use a fixed window of text. The paper demonstrates that the architecture is able to compete with the most successful supervised systems for WSD and that there is an abundance of possible improvements to take it to the current state of the art. In addition, it explores briefly the potential of combining different types of embeddings as input features; it also discusses possible ways for generating “artificial corpora” from knowledge bases – for the purpose of producing training data and in relation to possible applications of embedding lemmas and word senses in the same space.</abstract>
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%0 Conference Proceedings
%T Word Sense Disambiguation with Recurrent Neural Networks
%A Popov, Alexander
%Y Kovatchev, Venelin
%Y Temnikova, Irina
%Y Gencheva, Pepa
%Y Kiprov, Yasen
%Y Nikolova, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna
%F popov-2017-word
%X This paper presents a neural network architecture for word sense disambiguation (WSD). The architecture employs recurrent neural layers and more specifically LSTM cells, in order to capture information about word order and to easily incorporate distributed word representations (embeddings) as features, without having to use a fixed window of text. The paper demonstrates that the architecture is able to compete with the most successful supervised systems for WSD and that there is an abundance of possible improvements to take it to the current state of the art. In addition, it explores briefly the potential of combining different types of embeddings as input features; it also discusses possible ways for generating “artificial corpora” from knowledge bases – for the purpose of producing training data and in relation to possible applications of embedding lemmas and word senses in the same space.
%R 10.26615/issn.1314-9156.2017_004
%U https://doi.org/10.26615/issn.1314-9156.2017_004
%P 25-34

Markdown (Informal)

[Word Sense Disambiguation with Recurrent Neural Networks](https://doi.org/10.26615/issn.1314-9156.2017_004) (Popov, RANLP 2017)

• Word Sense Disambiguation with Recurrent Neural Networks (Popov, RANLP 2017)

ACL

• Alexander Popov. 2017. Word Sense Disambiguation with Recurrent Neural Networks. In Proceedings of the Student Research Workshop Associated with RANLP 2017, pages 25–34, Varna. INCOMA Ltd..