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      Distributional Models of Word Meaning

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      Annual Review of Linguistics
      Annual Reviews

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          A unified architecture for natural language processing

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            Predicting human brain activity associated with the meanings of nouns.

            The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.
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              SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation

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                Author and article information

                Journal
                Annual Review of Linguistics
                Annu. Rev. Linguist.
                Annual Reviews
                2333-9683
                2333-9691
                January 14 2018
                January 14 2018
                : 4
                : 1
                : 151-171
                Affiliations
                [1 ]Computational Linguistics Laboratory, University of Pisa, Pisa 56126, Italy;
                Article
                10.1146/annurev-linguistics-030514-125254
                6d7f9123-21f3-428e-87a8-3a82450f2fe7
                © 2018
                History

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