Divergence from Syntax to Linear Order in Ancient Greek Lexical Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

All natural languages allegedly tend to minimize the length (and thus, the number of crossings) of syntactic dependencies by arranging words into an adequate linear order. Focusing on Ancient Greek as a case of study, this paper demonstrates that this tendency is far from constant. The method consists in inducing a pair of networks from a text representing a given diachronic variety, using lemmas as nodes and word relations (either syntactic dependency or linear adjacency) as arcs. Although the pair members share some topological properties (such as small-world effect, scale-freeness and disassortative mixing), they also diverge in some respects. By comparing the divergence in the Classic variety with that of the Late variety through Spectrum Analysis, a change is observed. This phenomenon may lead to rethinking how language cognitive complexity is measured and whether it is equivalent for different (varieties of) languages. In particular, this paper proposes the existence of a trade-off between dependency length minimization and pragmatic principles ordering elements according to their givenness and topic/focus status.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference
PublisherAAAI Press
Pages541-546
Number of pages6
Publication statusPublished - 30 Mar 2016
EventThe Twenty-Ninth International Flairs Conference 2016
- Key Largo, United States
Duration: 16 May 201618 May 2016
Conference number: 29

Conference

ConferenceThe Twenty-Ninth International Flairs Conference 2016
Abbreviated titleFLAIRS 2016
Country/TerritoryUnited States
CityKey Largo
Period16/05/1618/05/16

Keywords / Materials (for Non-textual outputs)

  • Networks
  • Ancient Greek
  • Dependency Syntax
  • Linear Order
  • Spectrum Analysis
  • Complexity

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