Semantics and semantic priming

At present, Multilink implements a very simple semantics: Every word has its own concept. Cognates and translation equivalents are assumed to be mapped onto the same concept, which works fine. To simulate semantic priming effects (e.g., 0:LAW,5:ORDER), an association database is currently used. It turns out this leads to several problems. To clarify what is going on and improve the simulations, we are currently investigating homonymy and polysemy from different perspectives: considering concepts as symbolic representations, distributed feature sets, or vectors.

Associations

In Multilink, a semantic network can be activated to simulate spreading activation in semantic priming tasks. The connection strengths between concepts can be derived from association databases like the English Miami corpus by Nelson & McEvoy at and the Dutch Word Association Database from DeDeyne & Storms. At present, Multilink’s parameter settings are not fit for simulating semantic priming with any degree of precision.