The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Multimodal Distributional Semantics
Open accessAbstract
Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. Such models have been a success story of computational linguistics, being able to provide reliable estimates of semantic relatedness for the many semantic tasks requiring them. However, distributional models extract meaning information exclusively from text, which is an extremely impoverished basis compared to the rich perceptual sources that ground human semantic knowledge. We address the lack of perceptual grounding of distributional models by exploiting computer vision techniques that automatically identify discrete visual words in images, so that the distributional representation of a word can be extended to also encompass its co-occurrence with the visual words of images it is associated with. We propose a flexible architecture to integrate text- and image-based distributional information, and we show in a set of empirical tests that our integrated model is superior to the purely text-based approach, and it provides somewhat complementary semantic information with respect to the latter.
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Cites
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Cited by
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
Speer, R., Chin, J., & Havasi, C. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11164 · Cited by 2,312
Baroni, M., Dinu, G., & Kruszewski, G. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1023 · Cited by 1,364
Self-citationEthayarajh, K. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1006 · Cited by 841
Learning Distributed Representations of Sentences from Unlabelled Data
Hill, F., Cho, K., & Korhonen, A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1162 · Cited by 566
Metadata
- Authors
- Bruni, E., Tran, N. K., & Baroni, M.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2014-01-23
- Volume
- 49
- Pages
- 1-47
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2112184938
- Cited by
- 908
- References
- 113
- FWCI
- 75.67
- Type
- journal article
- Language
- en
- Open access
- diamond
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