Abstract
Research in computational semantics is increasingly guided by our understanding of human semantic processing. However, semantic models are typically studied in the context of natural language processing system performance. In this paper, we present a systematic evaluation and comparison of a range of widely-used, state-of-the-art semantic models in their ability to predict patterns of conceptual representation in the human brain. Our results provide new insights both for the design of computational semantic models and for further research in cognitive neuroscience.- Anthology ID:
- D17-1113
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1081–1091
- Language:
- URL:
- https://aclanthology.org/D17-1113
- DOI:
- 10.18653/v1/D17-1113
- Cite (ACL):
- Luana Bulat, Stephen Clark, and Ekaterina Shutova. 2017. Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1081–1091, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain (Bulat et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/D17-1113.pdf