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Towards a Map of the Syntactic Similarity of Languages

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Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10761))

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Abstract

In this paper we propose a computational method for determining the syntactic similarity between languages. We investigate multiple approaches and metrics, showing that the results are consistent across methods. We report results on 16 languages belonging to various language families. The analysis that we conduct is adaptable to any languages, as far as resources are available.

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Notes

  1. 1.

    The authors also present a brief history of the syntactic approaches and acknowledge the work of [30], a pioneer in this field and a forerunner of their approach.

  2. 2.

    We use dependency parsing [21], so we rely on dependency trees to compute the syntactic similarity.

  3. 3.

    Tagging and parsing accuracy for each language: Bg: 0.96,0.87; Cs: 0.98,0.82; Da: 0.95,0.82; De: 0.92,0.67; El: 0.96,0.82; En: 0.94,0.85; Es: 0.95,0.59; Et: 0.94,0.80; Fi: 0.93,0.75; Fr: 0.96,0.61; Hu: 0.92,0.79; It: 0.97,0.47; Nl: 0.89,0.77; Pt: 0.96,0.83; Ro: 0.95,0.82; Sv: 0.95,0.84.

  4. 4.

    We believe that our investigation is not negatively influenced by the choice of corpus because we are consistent across all experiments in terms of text gender and we report results obtained solely by comparison between languages on the same dataset. In future work, we intend to apply the proposed methods on other datasets as well (for example, the EUR-Lex Corpus [2]).

  5. 5.

    While the effect of translation cannot be denied, we rely on the fact that the interpreters/translators of Europarl are native speakers of the target language, which reduces the impact of the source language on translations significantly (as opposed to the translations performed by language learners, for example).

  6. 6.

    We repeated Exp. #1 and #2 using the rank distance [7] instead of the edit distance, and there were no significant differences in the results.

  7. 7.

    Developing of Romanian far from the big Romance kernel made the contact of Romanian with Romance languages difficult until the 18th century. Instead, from the 9th to the 17th century there was a significant cultural influence of the South Slavic languages (especially Old Slavic), due in part to the exclusive use of Old Church Slavonic for religious purposes, which lead to giving South Slavic “the status of a cultural superstrate language” [36].

  8. 8.

    www.bing.com/translator.

  9. 9.

    www.translate.google.com.

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Acknowledgments

We thank the anonymous reviewers for their helpful and constructive comments. This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS/CCCDI UEFISCDI, project number PN-III-P2-2.1-53BG/2016, within PNCDI III.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania

    Alina Maria Ciobanu & Liviu P. Dinu

  2. Human Language Technologies Research Center, University of Bucharest, Bucharest, Romania

    Alina Maria Ciobanu, Liviu P. Dinu & Andrea Sgarro

  3. Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy

    Andrea Sgarro

Authors
  1. Alina Maria Ciobanu
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  2. Liviu P. Dinu
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  3. Andrea Sgarro
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Correspondence to Liviu P. Dinu .

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  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Ciobanu, A.M., Dinu, L.P., Sgarro, A. (2018). Towards a Map of the Syntactic Similarity of Languages. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_44

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  • DOI: https://doi.org/10.1007/978-3-319-77113-7_44

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