Inferring semantic maps

  • 1 University of California Berkeley
  • 2 The University of Chicago
  • 3 Max Planck Institute for Psycholinguistics

Abstract

Semantic maps are a means of representing universal structure underlying semantic variation. However, no algorithm has existed for inferring a graph-based semantic map from cross-language data. Here, we note that this open problem is formally identical to the known problem of inferring a social network from disease outbreaks. From this identity it follows that semantic map inference is computationally intractable, but that an efficient approximation algorithm for it exists. We demonstrate that this algorithm produces sensible semantic maps from two existing bodies of data. We conclude that universal semantic graph structure can be automatically approximated from cross-language semantic data.

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Linguistic Typology publishes research on linguistic diversity and unity. It welcomes articles that report empirical findings about crosslinguistic variation, advance our understanding of the patterns of diversity, or refine typological methodology.

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