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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 41 1 Formalising Concepts as Grounded Abstractions ... Clark, Stephen; Lerchner, Alexander; von Glehn, Tamara. - : arXiv, 2021 BASE Show details 2 Grounded Language Learning Fast and Slow ... Hill, Felix; Tieleman, Olivier; von Glehn, Tamara. - : arXiv, 2020 BASE Show details 3 Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input ... Lazaridou, Angeliki; Hermann, Karl Moritz; Tuyls, Karl. - : arXiv, 2018 BASE Show details 4 Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing ... Maillard, Jean; Clark, Stephen. - : arXiv, 2018 BASE Show details 5 Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns ... Anderson, AJ; Kiela, Douwe; Clark, Stephen; Poesio, M. - : Apollo - University of Cambridge Repository, 2017 Abstract: Important advances have recently been made using computational semantic models to decode brain activity patterns associated with concepts; however, this work has almost exclusively focused on concrete nouns. How well these models extend to decoding abstract nouns is largely unknown. We address this question by applying state-of-the-art computational models to decode functional Magnetic Resonance Imaging (fMRI) activity patterns, elicited by participants reading and imagining a diverse set of both concrete and abstract nouns. One of the models we use is linguistic, exploiting the recent word2vec skipgram approach trained on Wikipedia. The second is visually grounded, using deep convolutional neural networks trained on Google Images. Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically. Splitting the fMRI data according to human concreteness ratings, we indeed observe that both models significantly decode the most ... : Stephen Clark is supported by ERC Starting Grant DisCoTex (306920). ... URL: https://www.repository.cam.ac.uk/handle/1810/263633 https://dx.doi.org/10.17863/cam.8991 BASE Hide details 6 Modelling metaphor with attribute-based semantics Bulat, L; Clark, Stephen; Shutova, Ekaterina. - : Association for Computational Linguistics, 2017. : Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017 BASE Show details 7 Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research ... Kiela, Douwe; Bulat, Luana; Vero, Anita L.. - : arXiv, 2016 BASE Show details 8 Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics ... Kiela, D; Vero, Anita; Clark, Stephen. - : Apollo - University of Cambridge Repository, 2016 BASE Show details 9 Multi-Modal Representations for Improved Bilingual Lexicon Learning ... Vulić, I; Kiela, D; Clark, Stephen. - : Apollo - University of Cambridge Repository, 2016 BASE Show details 10 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 BASE Show details 11 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 BASE Show details 12 RELPRON: A Relative Clause Evaluation Dataset for Compositional Distributional Semantics Rimell, Laura; Maillard, Jean; Polajnar, Tamara. - : MIT Press, 2016. : Computational Linguistics, 2016 BASE Show details 13 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 BASE Show details 14 Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks Xu, W; Auli, M; Clark, Stephen. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016 BASE Show details 15 Using Sentence Plausibility to Learn the Semantics of Transitive Verbs ... Polajnar, Tamara; Rimell, Laura; Clark, Stephen. - : arXiv, 2014 BASE Show details 16 The Frobenius anatomy of word meanings I: subject and object relative pronouns ... Sadrzadeh, Mehrnoosh; Clark, Stephen; Coecke, Bob. - : arXiv, 2014 BASE Show details 17 Participant Reference in Narrative Discourse: A Comparison of Three Methodologies Clark, Stephen. - : SIL International, 2012 BASE Show details 18 Concrete Sentence Spaces for Compositional Distributional Models of Meaning ... Grefenstette, Edward; Sadrzadeh, Mehrnoosh; Clark, Stephen. - : arXiv, 2011 BASE Show details 19 Mathematical foundations for a compositional distributional model of meaning Sadrzadeh, Mehrnoosh; Clark, Stephen; Coecke, Bob In: Linguistic analysis. - Vashon Island, Wash. : Linguistic Analysis 36 (2006) 1-4, 345-384 BLLDB OLC Linguistik Show details 20 Faster parsing by supertagger adaptation Kummerfeld, Jonathan K.; Roesner, Jessika; Dawborn, Tim... In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 345-355 BLLDB Show details Page: 1 2 3

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