@inproceedings{nazar-renau-2016-taxonomy,
    title = "A Taxonomy of {S}panish Nouns, a Statistical Algorithm to Generate it and its Implementation in Open Source Code",
    author = "Nazar, Rogelio  and
      Renau, Irene",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Grobelnik, Marko  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, Helene  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L16-1236/",
    pages = "1485--1492",
    abstract = "In this paper we describe our work in progress in the automatic development of a taxonomy of Spanish nouns, we offer the Perl implementation we have so far, and we discuss the different problems that still need to be addressed. We designed a statistically-based taxonomy induction algorithm consisting of a combination of different strategies not involving explicit linguistic knowledge. Being all quantitative, the strategies we present are however of different nature. Some of them are based on the computation of distributional similarity coefficients which identify pairs of sibling words or co-hyponyms, while others are based on asymmetric co-occurrence and identify pairs of parent-child words or hypernym-hyponym relations. A decision making process is then applied to combine the results of the previous steps, and finally connect lexical units to a basic structure containing the most general categories of the language. We evaluate the quality of the taxonomy both manually and also using Spanish Wordnet as a gold-standard. We estimate an average of 89.07{\%} precision and 25.49{\%} recall considering only the results which the algorithm presents with high degree of certainty, or 77.86{\%} precision and 33.72{\%} recall considering all results."
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    <name type="personal">
        <namePart type="given">Rogelio</namePart>
        <namePart type="family">Nazar</namePart>
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        <namePart type="given">Irene</namePart>
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            <namePart type="given">Khalid</namePart>
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        <name type="personal">
            <namePart type="given">Sara</namePart>
            <namePart type="family">Goggi</namePart>
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            <namePart type="given">Joseph</namePart>
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            <namePart type="given">Asuncion</namePart>
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%0 Conference Proceedings
%T A Taxonomy of Spanish Nouns, a Statistical Algorithm to Generate it and its Implementation in Open Source Code
%A Nazar, Rogelio
%A Renau, Irene
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F nazar-renau-2016-taxonomy
%X In this paper we describe our work in progress in the automatic development of a taxonomy of Spanish nouns, we offer the Perl implementation we have so far, and we discuss the different problems that still need to be addressed. We designed a statistically-based taxonomy induction algorithm consisting of a combination of different strategies not involving explicit linguistic knowledge. Being all quantitative, the strategies we present are however of different nature. Some of them are based on the computation of distributional similarity coefficients which identify pairs of sibling words or co-hyponyms, while others are based on asymmetric co-occurrence and identify pairs of parent-child words or hypernym-hyponym relations. A decision making process is then applied to combine the results of the previous steps, and finally connect lexical units to a basic structure containing the most general categories of the language. We evaluate the quality of the taxonomy both manually and also using Spanish Wordnet as a gold-standard. We estimate an average of 89.07% precision and 25.49% recall considering only the results which the algorithm presents with high degree of certainty, or 77.86% precision and 33.72% recall considering all results.
%U https://aclanthology.org/L16-1236/
%P 1485-1492
Markdown (Informal)
[A Taxonomy of Spanish Nouns, a Statistical Algorithm to Generate it and its Implementation in Open Source Code](https://aclanthology.org/L16-1236/) (Nazar & Renau, LREC 2016)
ACL