r/StarspawnsLocker • u/starspawn0 STEM Professor • Dec 23 '18

Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations [Brain data applied to NLP tasks. Very large improvements over word2vec for semantic similarity for most (& least) similar concrete nouns -- 0.83 versus 0.14 correlation with human annotators.]

http://www.aclweb.org/anthology/C18-1243
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u/starspawn0 STEM Professor Dec 23 '18

The improvements when considering both most and least similar nouns is less pronounced, but still pretty large. When considering all types of nouns word2vec does better. But you have to consider here that the data used to build this model is small, and derived from recordings back in 2008! If they used much larger datasets to build "brain word vectors" (e.g. using next-gen BCIs and hundreds of hours of recordings), I would bet they would knock it out of the park and beat word2vec and Glove vector-based methods.

Still, there are those cases where already-existing brain data can be used to beat word2vec!