Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
Marco Baroni, Georgiana Dinu, Germán Kruszewski · 2014
Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block.Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches.In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings.The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.