Similarity Semantics and Building Probabilistic Semantic Maps from Parallel Texts

This paper deals with statistical (non-implicational) semantic maps, built automatically using classical multidimensional scaling from a direct comparison of parallel text data (the Gospel according to Mark) in the domain of motion events (case/adpositions) in 153 languages from all continents in 190 parallel clauses. The practical objective of is to present one way (among other possible ways) how semantic maps can be built easily and fully automatically from large typological datasets (Section 3). Its methodological objective is to demonstrate that semantic maps can be built in various ways and that sampling of languages and small differences in the method chosen to build a semantic map can have a strong influence on the results (Section 4), which does not mean that semantic space is arbitrary, but rather that it is dynamic (having stretching and shrinking dimensions). The theoretical aim of the paper is to discuss similarity semantics, the implicit theoretical basis behind the semantic map approach, and to show that similarity semantics is not novel, but has a long-standing tradition in philosophy and psychology (Section 2).