Abstract
During sentence comprehension, humans adjust word meanings according to the combination of the concepts that occur in the sentence. This paper presents a neural network model called CEREBRA (Context-dEpendent meaning REpresentation in the BRAin) that demonstrates this process based on fMRI sentence patterns and the Concept Attribute Rep-resentation (CAR) theory. In several experiments, CEREBRA is used to quantify conceptual combination effect and demonstrate that it matters to humans. Such context-based representations could be used in future natural language processing systems allowing them to mirror human performance more accurately.- Anthology ID:
- 2020.cogalex-1.15
- Volume:
- Proceedings of the Workshop on the Cognitive Aspects of the Lexicon
- Month:
- December
- Year:
- 2020
- Address:
- Online
- Editors:
- Michael Zock, Emmanuele Chersoni, Alessandro Lenci, Enrico Santus
- Venue:
- CogALex
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 117–128
- Language:
- URL:
- https://aclanthology.org/2020.cogalex-1.15
- DOI:
- Cite (ACL):
- Nora Aguirre-Celis and Risto Miikkulainen. 2020. Characterizing Dynamic Word Meaning Representations in the Brain. In Proceedings of the Workshop on the Cognitive Aspects of the Lexicon, pages 117–128, Online. Association for Computational Linguistics.
- Cite (Informal):
- Characterizing Dynamic Word Meaning Representations in the Brain (Aguirre-Celis & Miikkulainen, CogALex 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.cogalex-1.15.pdf