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WordSim353 for Czech

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Text, Speech, and Dialogue (TSD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9924))

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Abstract

Human judgments of lexical similarity/relatedness are used as evaluation data for Vector Space Models, helping to judge how the distributional similarity captured by a given Vector Space Model correlates with human intuitions. A well established data set for the evaluation of lexical similarity/relatedness is WordSim353, along with its translations into several other languages. This paper presents its Czech translation and annotation, which is publicly available via the LINDAT-CLARIN repository at hdl.handle.net/11234/1-1713.

I thank Jan Hajič and Jana Straková for allowing me to use the Czech translations of WordSim353 they had gathered earlier.

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Notes

  1. 1.

    See Sect. 1.3 for a terminological clarification.

  2. 2.

    Although the relation between the pair members is often referred to as “relatedness”, which we also find more appropriate, the annotator instructions consistently use the word “similarity”. See also Sect. 1.3.

  3. 3.

    See http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/.

  4. 4.

    Cf. [6] and Sect. 1.3.

  5. 5.

    http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/.

  6. 6.

    [7] and [8].

  7. 7.

    A margin note: there is, though, a Czech derivative of líh equivalent to spirits, also typically used in plural: lihoviny, which the translators did not consider.

References

  1. Leviant, I., Reichart, R.: Separated by an Un-common Language: Towards Judgment Language Informed Vector Space Modeling. arXiv:508.00106v5 [cs.CL], 6 December 2015

  2. Hassan, S., Mihalcea, R.: Cross-lingual semantic relatedness using encyclopedic knowledge. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Singapore (2009)

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  3. Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing search in context: the concept revisited. ACM Trans. Inf. Syst. 20(1), 116–131 (2002)

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  4. Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Lang. Cogn. Process. 6(1), 1–28 (1991)

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  5. Hill, F., Reichart, R., Korhonen, A.: Simlex-999: evaluating semantic models with (genuine) similarity estimation. [cs.CL]. arXiv:1408.3456

  6. Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Pasca, M., Soroa, A.: A study on similarity and relatedness using distributional and wordnet-based approaches. In: Proceedings of NAACL-HLT 2009 (2011)

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  7. Hope, R.M.: Rmisc: Ryan Miscellaneous. R package version 1.5. https://CRAN.R-project.org/package=Rmisc

  8. R Core Team: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

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Acknowledgments

This project was supported by the Czech Science Foundation grant GA-15-20031S and has been using language resources developed and/or stored and/or distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth, and Sports of the Czech Republic (project LM2015071).

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Authors and Affiliations

  1. Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, Charles University, Malostranské náměstí 25, Praha 1, Prague, Czech Republic

    Silvie Cinková

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  1. Silvie Cinková
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Correspondence to Silvie Cinková .

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Editors and Affiliations

  1. Masaryk University , Brno, Czech Republic

    Petr Sojka

  2. Masaryk University , Brno, Czech Republic

    Aleš Horák

  3. Masaryk University , Brno, Czech Republic

    Ivan Kopeček

  4. Masaryk University , Brno, Czech Republic

    Karel Pala

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Cinková, S. (2016). WordSim353 for Czech. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2016. Lecture Notes in Computer Science(), vol 9924. Springer, Cham. https://doi.org/10.1007/978-3-319-45510-5_22

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  • DOI: https://doi.org/10.1007/978-3-319-45510-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45509-9

  • Online ISBN: 978-3-319-45510-5

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