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        <title>Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks</title>
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    <name type="personal">
        <namePart type="given">Arkadiusz</namePart>
        <namePart type="family">Janz</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <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>Word Sense Disambiguation remains a challenging NLP task. Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited to a narrow subset of identified senses. Recent advances in this area have shown that knowledge-based approaches are more scalable and obtain more promising results in all-words WSD scenarios. In this work we present a faster WSD algorithm based on the Monte Carlo approximation of sense probabilities given a context using constrained random walks over linked semantic networks. We show that the local semantic relatedness is mostly sufficient to successfully identify correct senses when an extensive knowledge base and a proper weighting scheme are used. The proposed methods are evaluated on English (SenseEval, SemEval) and Polish (Składnica, KPWr) datasets.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_061</identifier>
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%0 Conference Proceedings
%T Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks
%A Janz, Arkadiusz
%A Piasecki, Maciej
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F janz-piasecki-2019-word
%X Word Sense Disambiguation remains a challenging NLP task. Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited to a narrow subset of identified senses. Recent advances in this area have shown that knowledge-based approaches are more scalable and obtain more promising results in all-words WSD scenarios. In this work we present a faster WSD algorithm based on the Monte Carlo approximation of sense probabilities given a context using constrained random walks over linked semantic networks. We show that the local semantic relatedness is mostly sufficient to successfully identify correct senses when an extensive knowledge base and a proper weighting scheme are used. The proposed methods are evaluated on English (SenseEval, SemEval) and Polish (Składnica, KPWr) datasets.
%R 10.26615/978-954-452-056-4_061
%U https://aclanthology.org/R19-1061/
%U https://doi.org/10.26615/978-954-452-056-4_061
%P 516-525

Markdown (Informal)

[Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks](https://aclanthology.org/R19-1061/) (Janz & Piasecki, RANLP 2019)

• Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks (Janz & Piasecki, RANLP 2019)

ACL

• Arkadiusz Janz and Maciej Piasecki. 2019. Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 516–525, Varna, Bulgaria. INCOMA Ltd..