Work

Inferring semantic maps

Year: 2013

Type: article

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.

Source: Linguistic Typology

DOI: 10.1515/lity-2013-0003

Authors: Terry Regier, Naveen Khetarpal, Asifa Majid

Institutions: University of California, Berkeley, University of Chicago, Max Planck Institute for Psycholinguistics

Language: English

Topic: Categorization, perception, and language

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Spatial Intelligence and Learning Center (SILC), Spatial Intelligence and Learning Center (SILC)