Semantic Models as a Combination of Free Association Norms and Corpus-Based Correlations

We present computational models capable of understanding and conveying concepts based on word associations. 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 volunteers. 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.