Abstract
We present and take advantage of the inherent visualizability properties of words in visual corpora (the textual components of vision-language datasets) to compute concreteness scores for words. Our simple method does not require hand-annotated concreteness score lists for training, and yields state-of-the-art results when evaluated against concreteness scores lists and previously derived scores, as well as when used for metaphor detection.- Anthology ID:
- I17-2018
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 103–108
- Language:
- URL:
- https://aclanthology.org/I17-2018
- DOI:
- Cite (ACL):
- Gitit Kehat and James Pustejovsky. 2017. Integrating Vision and Language Datasets to Measure Word Concreteness. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 103–108, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Integrating Vision and Language Datasets to Measure Word Concreteness (Kehat & Pustejovsky, IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/I17-2018.pdf
- Data
- Flickr30k, MS COCO, Visual Genome