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Typology by Means of Language Networks: Applying Information Theoretic Measures to Morphological Derivation Networks

In: Towards an Information Theory of Complex Networks

Author

Listed:
  • Olga Abramov

    (University of Bielefeld)

  • Tatiana Lokot

    (University of Bielefeld, Faculty of Technology)

Abstract

In this chapter we present a network theoretic approach to linguistics. In particular, we introduce a network model of derivational morphology in languages. We focus on suffixation as a mechanism to derive new words from existing ones. We induce networks of natural language data consisting of words, derivation suffixes and parts of speech (PoS) as well as the relations between them. Measuring the entropy of these networks by means of so called information functionals we aim at capturing the variation between typologically different languages. In this way, we rely on the work of Dehmer (Appl Math Comput 201:82–94, 2008) who has introduced a framework for measuring the entropy of graphs. In addition, we compare several entropy measures recently presented for graphs. We check whether these measures allow us to distinguish between language networks on the one hand, and random networks on the other.We found out, that linguistic variation among languages can be captured by investigating the topology of the underlying networks. Further, information functionals based on distributions of topological properties turned out to be better discriminators than those that are based on properties of single vertices.

Suggested Citation

  • Olga Abramov & Tatiana Lokot, 2011. "Typology by Means of Language Networks: Applying Information Theoretic Measures to Morphological Derivation Networks," Springer Books, in: Matthias Dehmer & Frank Emmert-Streib & Alexander Mehler (ed.), Towards an Information Theory of Complex Networks, edition 1, chapter 0, pages 321-346, Springer.
    • Handle: RePEc:spr:sprchp:978-0-8176-4904-3_11
    DOI: 10.1007/978-0-8176-4904-3_11
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