University of Cambridge > Talks.cam > NLIP Seminar Series > BrainNet: Using Brain (and Corpus) Data to Investigate Conceptual Knowledge

BrainNet: Using Brain (and Corpus) Data to Investigate Conceptual Knowledge

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Existing electronic repositories of lexical and commonsense knowledge such as ConceptNet, Cyc, FrameNet, and especially WordNet (Fellbaum,1998), have had a dramatic and positive impact on Artificial Intelligence (AI) and Human Language Technology (HLT) research, making it possible to carry out the first large-scale semantic analyses of text and some simple forms of inference. Nowadays there are few semantic interpretation systems that do not use WordNet. However, the widespread application of these resources has also highlighted their limitations. One example of problem often mentioned in the literature is that the taxonomy structure of WordNet, which plays a crucial role e.g., in the calculation of lexical distance metrics, is based in part on scientific taxonomies for specific domains (e.g., animals, plants), in part on linguistic intuitions.

The hypothesis underlying the BrainNet project is that the dramatic advances in our knowledge of concepts arising from interdisciplinary research of the last thirty years pave the way to the development of a lexical resource of a novel type that may overcome the limits just discussed: an electronic dictionary that directly mirrors the mental lexicon, modelled on the basis of recordings of brain activity using contemporary neuroimaging techniques (EEG, MEG and fMRI). The goal of the BrainNet project is to translate cutting-edge theories and methods from cognitive psychology, computational linguistics, and cognitive neuroscience into a new model for lexical-semantic representation and organization.

Joint work with Andrew Anderson, Brian Murphy, Yuqiao Gu, Marco Baroni, and Yuan Tao.

This talk is part of the NLIP Seminar Series series.

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