Towards Never Ending Language Learning for Morphologically Rich Languages

Kseniya Buraya; Lidia Pivovarova; Sergey Budkov; Andrey Filchenkov

doi:10.18653/v1/W17-1417

Towards Never Ending Language Learning for Morphologically Rich Languages

Kseniya Buraya, Lidia Pivovarova, Sergey Budkov, Andrey Filchenkov

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Abstract

This work deals with ontology learning from unstructured Russian text. We implement one of components Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologicaly rich free-word-order language. We demonstrate that this method may be successfully applied to Russian data. In addition we perform several additional experiments comparing different settings of the training process. We demonstrate that utilizing of morphological features significantly improves the system precision while using of seed patterns helps to improve the coverage.

Anthology ID:: W17-1417
Volume:: Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing
Month:: April
Year:: 2017
Address:: Valencia, Spain
Editors:: Tomaž Erjavec, Jakub Piskorski, Lidia Pivovarova, Jan Šnajder, Josef Steinberger, Roman Yangarber
Venue:: BSNLP
SIG:: SIGSLAV
Publisher:: Association for Computational Linguistics
Note:
Pages:: 108–118
Language:
URL:: https://aclanthology.org/W17-1417/
DOI:: 10.18653/v1/W17-1417
Bibkey:
Cite (ACL):: Kseniya Buraya, Lidia Pivovarova, Sergey Budkov, and Andrey Filchenkov. 2017. Towards Never Ending Language Learning for Morphologically Rich Languages. In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing, pages 108–118, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):: Towards Never Ending Language Learning for Morphologically Rich Languages (Buraya et al., BSNLP 2017)
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PDF:: https://aclanthology.org/W17-1417.pdf

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@inproceedings{buraya-etal-2017-towards,
    title = "Towards Never Ending Language Learning for Morphologically Rich Languages",
    author = "Buraya, Kseniya  and
      Pivovarova, Lidia  and
      Budkov, Sergey  and
      Filchenkov, Andrey",
    editor = "Erjavec, Toma{\v{z}}  and
      Piskorski, Jakub  and
      Pivovarova, Lidia  and
      {\v{S}}najder, Jan  and
      Steinberger, Josef  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-1417/",
    doi = "10.18653/v1/W17-1417",
    pages = "108--118",
    abstract = "This work deals with ontology learning from unstructured Russian text. We implement one of components Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologicaly rich free-word-order language. We demonstrate that this method may be successfully applied to Russian data. In addition we perform several additional experiments comparing different settings of the training process. We demonstrate that utilizing of morphological features significantly improves the system precision while using of seed patterns helps to improve the coverage."
}

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%0 Conference Proceedings
%T Towards Never Ending Language Learning for Morphologically Rich Languages
%A Buraya, Kseniya
%A Pivovarova, Lidia
%A Budkov, Sergey
%A Filchenkov, Andrey
%Y Erjavec, Tomaž
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F buraya-etal-2017-towards
%X This work deals with ontology learning from unstructured Russian text. We implement one of components Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologicaly rich free-word-order language. We demonstrate that this method may be successfully applied to Russian data. In addition we perform several additional experiments comparing different settings of the training process. We demonstrate that utilizing of morphological features significantly improves the system precision while using of seed patterns helps to improve the coverage.
%R 10.18653/v1/W17-1417
%U https://aclanthology.org/W17-1417/
%U https://doi.org/10.18653/v1/W17-1417
%P 108-118

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Markdown (Informal)

[Towards Never Ending Language Learning for Morphologically Rich Languages](https://aclanthology.org/W17-1417/) (Buraya et al., BSNLP 2017)

Towards Never Ending Language Learning for Morphologically Rich Languages (Buraya et al., BSNLP 2017)

ACL

Kseniya Buraya, Lidia Pivovarova, Sergey Budkov, and Andrey Filchenkov. 2017. Towards Never Ending Language Learning for Morphologically Rich Languages. In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing, pages 108–118, Valencia, Spain. Association for Computational Linguistics.