Predicting Brain Activation with WordNet Embeddings

João António Rodrigues, Ruben Branco, João Silva, Chakaveh Saedi, António Branco


Abstract
The task of taking a semantic representation of a noun and predicting the brain activity triggered by it in terms of fMRI spatial patterns was pioneered by Mitchell et al. 2008. That seminal work used word co-occurrence features to represent the meaning of the nouns. Even though the task does not impose any specific type of semantic representation, the vast majority of subsequent approaches resort to feature-based models or to semantic spaces (aka word embeddings). We address this task, with competitive results, by using instead a semantic network to encode lexical semantics, thus providing further evidence for the cognitive plausibility of this approach to model lexical meaning.
Anthology ID:
W18-2801
Volume:
Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing
Month:
July
Year:
2018
Address:
Melbourne
Venues:
ACL | CogACLL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–5
Language:
URL:
https://www.aclweb.org/anthology/W18-2801
DOI:
10.18653/v1/W18-2801
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-2801.pdf