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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...38 39 40 41 42 Hits 821 – 828 of 828 821 The Live Principle of Compositionality ∗ Paul J. E. Dekker In: http://staff.science.uva.nl/~ulle/teaching/lolaco/2013/papers/dekker.pdf BASE Show details 822 and corpus-based methods: On the compositionality of English V NP-idioms In: http://www.lingtechcomm.unt.edu/~swulff/research/Wulff (2010).pdf BASE Show details 823 Understanding and Improving Content Markup for the Web: from the Perspectives of Formal Linguistics, Algebraic Logics, and Cognitive Science Ladislav J. Kohout; Andreas Strotmann In: http://www.cs.fsu.edu/~strotman/publications/is98-paper.ps.gz BASE Show details 824 A Neural Theory of Language and Embodied Construction Grammar Jerome Feldman; Ellen Dodge; John Bryant In: http://www.icsi.berkeley.edu/%7Ejbryant/FeldmanDodgeBryantOxford.pdf BASE Show details 825 SICK through the SemEval glasses: lesson learned from the evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment Bentivogli, Luisa; Bernardi, Raffaella; Marelli, Marco. - : Springer BASE Show details 826 Don’t blame distributional semantics if it can’t do entailment Westera, Matthijs. - : ACL (Association for Computational Linguistics) BASE Show details 827 Linguistic generalization and compositionality in modern artificial neural networks. Baroni, Marco. - : Royal Society Abstract: In the last decade, deep artificial neural networks have achieved astounding performance in many natural language processing tasks. Given the high productivity of language, these models must possess e ective generalization abilities. It is widely assumed that humans handle linguistic productivity by means of algebraic compositional rules: Are deep networks similarly compositional? After reviewing the main innovations characterizing current deep language processing networks, I discuss a set of studies suggesting that deep networks are capable of subtle grammar-dependent generalizations, but also that they do not rely on systematic compositional rules. I argue that the intriguing behaviour of these devices (still awaiting a full understanding) should be of interest to linguists and cognitive scientists, as it o ers a new perspective on possible computational strategies to deal with linguistic productivity beyond rule-based compositionality, and it might lead to new insights into the less systematic generalization patterns that also appear in natural language. Keyword: Artificial neural networks; Compositionality; Deep learning; Linguistic productivity URL: https://doi.org/10.1098/rstb.2019.0307 http://hdl.handle.net/10230/43459 BASE Hide details 828 Compositional Distributional Semantics with Syntactic Dependencies and Selectional Preferences Gamallo Otero, Pablo. - : MDPI BASE Show details Page: 1...38 39 40 41 42

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