Semantic models as a combination of free association norms and corpus-based correlations

We present computational models capable of under-standing and conveying concepts based on word asso-ciations. We discover word associations automatically using corpus-based semantic models with Wikipedia as the corpus. The best model effectively combines corpus-based models with preexisting databases of free association norms gathered from human volun-teers. We use this model to play human-directed and computer-directed word guessing games (games with a purpose similar to Catch Phrase or Taboo) and show that this model can measurably convey and understand some aspect of word meaning. The results highlight the fact that human-derived word associations and corpus-derived word associations can play complementary roles in semantic models. I