Paper: Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning

ACL ID P14-1046
Title Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Vector space models (VSMs) represent word meanings as points in a high dimen- sional space. VSMs are typically created using a large text corpora, and so repre- sent word semantics as observed in text. We present a new algorithm (JNNSE) that can incorporate a measure of semantics not previously used to create VSMs: brain activation data recorded while people read words. The resulting model takes advan- tage of the complementary strengths and weaknesses of corpus and brain activation data to give a more complete representa- tion of semantics. Evaluations show that the model 1) matches a behavioral mea- sure of semantics more closely, 2) can be used to predict corpus data for unseen words and 3) has predictive power that generalizes across brain imaging technolo- gies and across subjects. We...