  Machines have achieved a broad and growing set of linguistic competencies,
thanks to recent progress in Natural Language Processing (NLP). Psychologists
have shown increasing interest in such models, comparing their output to
psychological judgments such as similarity, association, priming, and
comprehension, raising the question of whether the models could serve as
psychological theories. In this article, we compare how humans and machines
represent the meaning of words. We argue that contemporary NLP systems are
fairly successful models of human word similarity, but they fall short in many
other respects. Current models are too strongly linked to the text-based
patterns in large corpora, and too weakly linked to the desires, goals, and
beliefs that people express through words. Word meanings must also be grounded
in perception and action and be capable of flexible combinations in ways that
current systems are not. We discuss more promising approaches to grounding NLP
systems and argue that they will be more successful with a more human-like,
conceptual basis for word meaning.
