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From language identification to language distance

Author

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  • Gamallo, Pablo
  • Pichel, José Ramom
  • Alegria, Iñaki

Abstract

In this paper, we define two quantitative distances to measure how far apart two languages are. The distance measure that we have identified as more accurate is based on the perplexity of n-gram models extracted from text corpora. An experiment to compare forty-four European languages has been performed. For this purpose, we computed the distances for all the possible language pairs and built a network whose nodes are languages and edges are distances. The network we have built on the basis of linguistic distances represents the current map of similarities and divergences among the main languages of Europe.

Suggested Citation

  • Gamallo, Pablo & Pichel, José Ramom & Alegria, Iñaki, 2017. "From language identification to language distance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 152-162.
    • Handle: RePEc:eee:phsmap:v:484:y:2017:i:c:p:152-162
    DOI: 10.1016/j.physa.2017.05.011
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    References listed on IDEAS

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    1. Petroni, Filippo & Serva, Maurizio, 2010. "Measures of lexical distance between languages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2280-2283.
    2. Chiswick, Barry R. & Miller, Paul W., 2004. "Linguistic Distance: A Quantitative Measure of the Distance Between English and Other Languages," IZA Discussion Papers 1246, Institute of Labor Economics (IZA).
    3. Gao, Yuyang & Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Comparison of directed and weighted co-occurrence networks of six languages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 579-589.
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    5. Wichmann, Søren & Holman, Eric W. & Bakker, Dik & Brown, Cecil H., 2010. "Evaluating linguistic distance measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3632-3639.
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    Cited by:

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