Abstract
In this paper, we propose a novel method for multimodal word embedding, which exploit a generalized framework of multi-view spectral graph embedding to take into account visual appearances or scenes denoted by words in a corpus. We evaluated our method through word similarity tasks and a concept-to-image search task, having found that it provides word representations that reflect visual information, while somewhat trading-off the performance on the word similarity tasks. Moreover, we demonstrate that our method captures multimodal linguistic regularities, which enable recovering relational similarities between words and images by vector arithmetics.- Anthology ID:
- W17-2405
- Volume:
- Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Martin Riedl, Swapna Somasundaran, Goran Glavaš, Eduard Hovy
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 39–44
- Language:
- URL:
- https://aclanthology.org/W17-2405
- DOI:
- 10.18653/v1/W17-2405
- Cite (ACL):
- Kazuki Fukui, Takamasa Oshikiri, and Hidetoshi Shimodaira. 2017. Spectral Graph-Based Method of Multimodal Word Embedding. In Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing, pages 39–44, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Spectral Graph-Based Method of Multimodal Word Embedding (Fukui et al., TextGraphs 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/W17-2405.pdf
- Data
- NUS-WIDE, Visual Question Answering