Evaluating vector space models using human semantic priming results
Allyson Ettinger, Tal Linzen · 2016
Vector space models of word representation are often evaluated using human similarity ratings.Those ratings are elicited in explicit tasks and have well-known subjective biases.As an alternative, we propose evaluating vector spaces using implicit cognitive measures.We focus in particular on semantic priming, exploring the strengths and limitations of existing datasets, and propose ways in which those datasets can be improved.