Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources

Darja Fišer; Senja Pollak; Špela Vintar

Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources

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Abstract

The paper presents an innovative approach to extract Slovene definition candidates from domain-specific corpora using morphosyntactic patterns, automatic terminology recognition and semantic tagging with wordnet senses. First, a classification model was trained on examples from Slovene Wikipedia which was then used to find well-formed definitions among the extracted candidates. The results of the experiment are encouraging, with accuracy ranging from 67% to 71%. The paper also addresses some drawbacks of the approach and suggests ways to overcome them in future work.

Anthology ID:: L10-1089
Volume:: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:: May
Year:: 2010
Address:: Valletta, Malta
Editors:: Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, Daniel Tapias
Venue:: LREC
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Publisher:: European Language Resources Association (ELRA)
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URL:: http://www.lrec-conf.org/proceedings/lrec2010/pdf/141_Paper.pdf
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Cite (ACL):: Darja Fišer, Senja Pollak, and Špela Vintar. 2010. Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Cite (Informal):: Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources (Fišer et al., LREC 2010)
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PDF:: http://www.lrec-conf.org/proceedings/lrec2010/pdf/141_Paper.pdf

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@inproceedings{fiser-etal-2010-learning,
    title = "Learning to Mine Definitions from {S}lovene Structured and Unstructured Knowledge-Rich Resources",
    author = "Fi{\v{s}}er, Darja  and
      Pollak, Senja  and
      Vintar, {\v{S}}pela",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Odijk, Jan  and
      Piperidis, Stelios  and
      Rosner, Mike  and
      Tapias, Daniel",
    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
    month = may,
    year = "2010",
    address = "Valletta, Malta",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L10-1089/",
    abstract = "The paper presents an innovative approach to extract Slovene definition candidates from domain-specific corpora using morphosyntactic patterns, automatic terminology recognition and semantic tagging with wordnet senses. First, a classification model was trained on examples from Slovene Wikipedia which was then used to find well-formed definitions among the extracted candidates. The results of the experiment are encouraging, with accuracy ranging from 67{\%} to 71{\%}. The paper also addresses some drawbacks of the approach and suggests ways to overcome them in future work."
}

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%0 Conference Proceedings
%T Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources
%A Fišer, Darja
%A Pollak, Senja
%A Vintar, Špela
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F fiser-etal-2010-learning
%X The paper presents an innovative approach to extract Slovene definition candidates from domain-specific corpora using morphosyntactic patterns, automatic terminology recognition and semantic tagging with wordnet senses. First, a classification model was trained on examples from Slovene Wikipedia which was then used to find well-formed definitions among the extracted candidates. The results of the experiment are encouraging, with accuracy ranging from 67% to 71%. The paper also addresses some drawbacks of the approach and suggests ways to overcome them in future work.
%U https://aclanthology.org/L10-1089/

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Markdown (Informal)

[Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources](https://aclanthology.org/L10-1089/) (Fišer et al., LREC 2010)

Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources (Fišer et al., LREC 2010)

ACL

Darja Fišer, Senja Pollak, and Špela Vintar. 2010. Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).