Abstract
Concreteness of words has been studied extensively in psycholinguistic literature. A number of datasets have been created with average values for perceived concreteness of words. We show that we can train a regression model on these data, using word embeddings and morphological features, that can predict these concreteness values with high accuracy. We evaluate the model on 7 publicly available datasets. Only for a few small subsets of these datasets prediction of concreteness values are found in the literature. Our results clearly outperform the reported results for these datasets.- Anthology ID:
- W19-0415
- Volume:
- Proceedings of the 13th International Conference on Computational Semantics - Long Papers
- Month:
- May
- Year:
- 2019
- Address:
- Gothenburg, Sweden
- Venue:
- IWCS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 176–187
- Language:
- URL:
- https://aclanthology.org/W19-0415
- DOI:
- 10.18653/v1/W19-0415
- Cite (ACL):
- Jean Charbonnier and Christian Wartena. 2019. Predicting Word Concreteness and Imagery. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 176–187, Gothenburg, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Word Concreteness and Imagery (Charbonnier & Wartena, IWCS 2019)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/W19-0415.pdf