Toward a universal decoder of linguistic meaning from brain activation

Humans have the unique capacity to translate thoughts into words, and to infer others' thoughts from their utterances. This ability is based on mental representations of meaning that can be mapped to language, but to which we have no direct access. The approach to meaning representation that currently dominates the field of natural language processing relies on distributional semantic models, which rest on the simple yet powerful idea that words similar in meaning occur in similar linguistic contexts1. A word is represented as a semantic vector in a high-dimensional space, where similarity between two word vectors reflects similarity of the contexts in which those words appear in the language2. More recently, these models have been extended beyond single words to express meanings of phrases and sentences5,6,7, and the resulting representations predict human similarity judgments for phrase- and sentence-level paraphrases8,9.