Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models
- Submitting institution
-
Goldsmiths' College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 3512
- Type
- D - Journal article
- DOI
-
10.1162/tacl_a_00307
- Title of journal
- Transactions of the Association for Computational Linguistics
- Article number
- -
- First page
- 231
- Volume
- 8
- Issue
- -
- ISSN
- 2307-387X
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2020
- URL
-
http://research.gold.ac.uk/id/eprint/28867/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- There is interest within the NLP community in evaluating the ability of neural-network based distributional semantic models to capture human meaning representation in the brain. The work is the first to investigate metaphor processing in the brain in this context showing that semantic models can capture differences in literal and metaphoric uses of words in the brain. The work is significant as it is an important step towards interpreting the representations learnt by neural networks and how they incorporate context, which can be leveraged to improve these networks. The work was presented at ACL -- a top computational linguistics conference.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -