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1
Visually grounded and textual semantic models differentially decode brain activity associated with concrete and abstract nouns
POESIO, M; ANDERSON, A; Clark, S. - : Association for Computational Linguistics, 2018
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2
Supervised learning of universal sentence representations from natural language inference data
Conneau, A.; Kiela, D.; Schwenk, H.. - : Association for Computational Linguistics, 2017
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3
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment ...
Vulić, I; Gerz, D; Kiela, D. - : Apollo - University of Cambridge Repository, 2017
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4
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
Vulić, I; Gerz, D; Kiela, D. - : MIT Press, 2017. : Computational Linguistics, 2017
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5
Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
Rei, Marek; Bulat, LT; Kiela, D. - : Association for Computational Linguistics, 2017. : EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings, 2017
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6
Evaluation by association: A systematic study of quantitative word association evaluation
Vulić, I; Kiela, D; Korhonen, Anna-Leena. - : Association for Computational Linguistics, 2017. : 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference, 2017
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7
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics ...
Kiela, D; Vero, Anita; Clark, Stephen. - : Apollo - University of Cambridge Repository, 2016
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8
Multi-Modal Representations for Improved Bilingual Lexicon Learning ...
Vulić, I; Kiela, D; Clark, Stephen. - : Apollo - University of Cambridge Repository, 2016
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9
Multi-Modal Representations for Improved Bilingual Lexicon Learning
Vulić, I; Kiela, D; Clark, Stephen. - : Association for Computational Linguistics, 2016. : http://acl2016.org/, 2016. : Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016
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10
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics
Kiela, D; Vero, Anita; Clark, Stephen. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016
Abstract: Multi-modal distributional models learn grounded representations for improved performance in semantics. Deep visual representations, learned using convolutional neural networks, have been shown to achieve particularly high performance. In this study, we systematically compare deep visual representation learning techniques, experimenting with three well-known network architectures. In addition, we explore the various data sources that can be used for retrieving relevant images, showing that images from search engines perform as well as, or better than, those from manually crafted resources such as ImageNet. Furthermore, we explore the optimal number of images and the multi-lingual applicability of multi-modal semantics. We hope that these findings can serve as a guide for future research in the field. ; Anita Verõ is supported by the Nuance Foundation Grant: Learning Type-Driven Distributed Representations of Language. Stephen Clark is supported by the ERC Starting Grant: DisCoTex (306920).
URL: https://doi.org/10.17863/CAM.9060
https://www.repository.cam.ac.uk/handle/1810/263697
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11
Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps
Bulat, L; Kiela, D; Clark, Stephen. - : Association for Computational Linguistics, 2016. : http://www.aclweb.org/anthology/N/N16/, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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