Abstract
Vector space models (VSMs) represent
word meanings as points in a high dimensional
space. VSMs are typically created
using a large text corpora, and so represent
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 advantage
of the complementary strengths and
weaknesses of corpus and brain activation
data to give a more complete representation
of semantics. Evaluations show that
the model 1) matches a behavioral measure
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 technologies
and across subjects. We believe that
the model is thus a more faithful representation
of mental vocabularies.
Original language | English |
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Title of host publication | The 52nd Annual Meeting of the Association for Computational Linguistics: Proceedings of the conference volume 1: Long papers |
Place of Publication | Stroudsburg |
Publisher | The Association for Computational Linguistics |
Pages | 489-499 |
Number of pages | 11 |
Volume | 1 |
ISBN (Electronic) | 9781937284725 |
Publication status | Published - Jun 2014 |
Event | The 52nd Annual Meeting of the Association for Computational Linguistics - Baltimore, United States Duration: 22 Jun 2014 → 27 Jun 2014 |
Conference
Conference | The 52nd Annual Meeting of the Association for Computational Linguistics |
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Country/Territory | United States |
City | Baltimore |
Period | 22/06/2014 → 27/06/2014 |