Abstract
How do people understand the meaning of the word “small” when used to describe a mosquito, a church, or a planet? While humans have a remarkable ability to form meanings by combining existing concepts, modeling this process is challenging. This paper addresses that challenge through CEREBRA (Context-dEpendent meaning REpresentations in the BRAin) neural network model. CEREBRA characterizes how word meanings dynamically adapt in the context of a sentence by decomposing sentence fMRI into words and words into embodied brain-based semantic features. It demonstrates that words in different contexts have different representations and the word meaning changes in a way that is meaningful to human subjects. CEREBRA’s context-based representations can potentially be used to make NLP applications more human-like.- Anthology ID:
- 2021.semspace-1.1
- Volume:
- Proceedings of the 2021 Workshop on Semantic Spaces at the Intersection of NLP, Physics, and Cognitive Science (SemSpace)
- Month:
- June
- Year:
- 2021
- Address:
- Groningen, The Netherlands
- Venue:
- SemSpace
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–11
- Language:
- URL:
- https://aclanthology.org/2021.semspace-1.1
- DOI:
- Cite (ACL):
- Nora Aguirre-Celis and Risto Miikkulainen. 2021. Understanding the Semantic Space: How Word Meanings Dynamically Adapt in the Context of a Sentence. In Proceedings of the 2021 Workshop on Semantic Spaces at the Intersection of NLP, Physics, and Cognitive Science (SemSpace), pages 1–11, Groningen, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- Understanding the Semantic Space: How Word Meanings Dynamically Adapt in the Context of a Sentence (Aguirre-Celis & Miikkulainen, SemSpace 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.semspace-1.1.pdf