Evaluating vector space models using human semantic priming results

@inproceedings{Ettinger2016EvaluatingVS,
  title={Evaluating vector space models using human semantic priming results},
  author={Allyson Ettinger and Tal Linzen},
  booktitle={RepEval@ACL},
  year={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. 

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