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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 4 5 6...42 Hits 21 – 40 of 828 21 Machine learning methods for vector-based compositional semantics ... Maillard, Jean. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 22 Sintassi delle frasi idiomatiche e delle frasi libere dell’italiano: differenze o similarità? Vietri, Simonetta In: Lingue e Linguaggi; Volume 33 (2019); 317-335 (2020) BASE Show details 23 Modeling Conventionalization and Predictability within MWEs at the Brain Level Bhattasali, Shohini; Popa-Fabre, Murielle; Pallier, Christophe... In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 24 Tensor Product Decomposition Networks: Uncovering Representations of Structure Learned by Neural Networks McCoy, Richard T; Linzen, Tal; Dunbar, Ewan; Smolensky, Paul In: Proceedings of the Society for Computation in Linguistics (2020) Abstract: We introduce an analysis technique for understanding compositional structure present in the vector representations used by neural networks. The inner workings of neural networks are notoriously difficult to understand, and in particular it is far from clear how they manage to perform remarkably well on tasks that depend on compositional structure even though they use continuous vector representations with no obvious compositional structure. Using our analysis technique, we show that the representations of these models can be closely approximated by Tensor Product Representations, a type of interpretable structure that lends significant insight into the workings of these hard-to-interpret models. Keyword: compositionality; Computational Linguistics; neural networks; symbolic structure; tensor product representations URL: https://scholarworks.umass.edu/scil/vol3/iss1/55 https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1130&context=scil BASE Hide details 25 The meaning of language Ebbers, Melissa; Martin, Robert M.; Savage, Heidi. - London, England : The MIT Press, 2019 BLLDB UB Frankfurt Linguistik Show details 26 Open compositionality : toward a new methodology of language García Ramírez, Eduardo. - London : Lexington Books, 2019 BLLDB UB Frankfurt Linguistik Show details 27 Light verb semantics in the 'International corpus of English': onomasiological variation, identity evidence and degrees of lightness Mehl, Seth In: English language and linguistics. - Cambridge : Cambridge Univ. Press 23 (2019) 1, 55-80 BLLDB Show details 28 Necessity lost : modality and logic in early analytic philosophy ; Volume 1 Shieh, Sanford. - Oxford, United Kingdom : Oxford University Press, 2019 BLLDB UB Frankfurt Linguistik Show details 29 How flexible are idioms? : A corpus-based study Fellbaum, Christiane In: Linguistics. - Berlin [u.a.] : Mouton de Gruyter 57 (2019) 4, 735-767 BLLDB Show details 30 Methods for taking semantic graphs apart and putting them back together again ... Groschwitz, Jonas. - : Universität des Saarlandes, 2019 BASE Show details 31 Methods for taking semantic graphs apart and putting them back together again Groschwitz, Jonas. - : Sydney, Australia : Macquarie University, 2019 BASE Show details 32 Sintassi delle frasi idiomatiche e delle frasi libere dell'italiano: differenze o similarità? ... Vietri, Simonetta. - : University of Salento, 2019 BASE Show details 33 Formulaic Thought and Expression in Linguistic Politeness ; Culture, Cognition, Discourse and Grammar: Cognitive Considerations on Formulaic Language Vogel, Carl. - : Peter Lang, 2019 BASE Show details 34 Sentences as systems. The principle of compositionality and its limits Frigerio, Aldo (orcid:0000-0003-4698-2110). - : Springer International Publishing, 2019. : country:CHE, 2019. : place:ZURICH, 2019 BASE Show details 35 Machine learning methods for vector-based compositional semantics Maillard, Jean. - : University of Cambridge, 2019. : Department of Computer Science and Technology, 2019. : St John's College, 2019 BASE Show details 36 Induction and interaction in the evolution of language and conceptual structure Carr, Jon William. - : The University of Edinburgh, 2019 BASE Show details 37 Methods for taking semantic graphs apart and putting them back together again Groschwitz, Jonas. - : Saarländische Universitäts- und Landesbibliothek, 2019 BASE Show details 38 What Do Neural Networks Actually Learn, When They Learn to Identify Idioms? Senaldi, Marco Silvio Giuseppe; Bizzoni, Yuri; Lenci, Alessandro In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 39 Meaning in linguistic interaction : semantics, metasemantics, philosophy of language Jaszczolt, Katarzyna. - Oxford : Oxford University Press, 2018 BLLDB UB Frankfurt Linguistik Show details 40 Puzzles of reference Cappelen, Herman; Dever, Josh. - Oxford, United Kingdom : Oxford University Press, 2018 BLLDB UB Frankfurt Linguistik Show details Page: 1 2 3 4 5 6...42

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