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Hits 1 – 13 of 13

1
A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face
Chen, J; Chen, Y; Cohn, AG. - : Elsevier, 2022
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2
Categorisation, Typicality & Object-Specific Features in Spatial Referring Expressions
Richard-Bollans, A; Gómez Álvarez, L; Cohn, AG. - : Association for Computational Linguistics, 2020
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3
Modelling the Polysemy of Spatial Prepositions in Referring Expressions
Richard-Bollans, A; Gomez Alvarez, L; Cohn, AG. - : IJCAI Organization, 2020
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4
Investigating the Dimensions of Spatial Language
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5
The Role of Pragmatics in Solving the Winograd Schema Challenge
Richard-Bollans, AL; Gomez Alvarez, L; Cohn, AG. - : CEUR Workshop Proceedings, 2018
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6
Learning of Object Properties, Spatial Relations, and Actions for Embodied Agents from Language and Vision
Alomari, M; Duckworth, P; Hogg, DC. - : AAAI Press, 2017
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7
Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands
Alomari, M; Duckworth, P; Hawasly, M. - : The Association for Computational Linguistics, 2017
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8
Natural Language Acquisition and Grounding for Embodied Robotic Systems
Duckworth, P; Al-Omari, M; Hogg, DC. - : Association for the Advancement of Artificial Intelligence, 2017
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9
Grounding language in perception for scene conceptualization in autonomous robots
Dubba, KSR; De Oliveira, MR; Lim, GH. - : AI Access Foundation, 2014
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10
Interactive semantic feedback for intuitive ontology authoring
Denaux, R; Thakker, DA; Dimitrova, V. - : IOS Press, 2012
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11
From Video to RCC8: Exploiting a Distance Based Semantics to Stabilise the Interpretation of Mereotopological Relations
Sridhar, M; Cohn, AG; Hogg, DC. - : Springer, 2011
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12
The automated evaluation of inferred word classifications
Hughes, J; Atwell, E. - : John Wiley & Sons, 1994
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13
Online perceptual learning and natural language acquisition for autonomous robots
Alomari, M; Li, F; Hogg, DC; Cohn, AG. - : Elsevier, 1479
Abstract: In this work, the problem of bootstrapping knowledge in language and vision for autonomous robots is addressed through novel techniques in grammar induction and word grounding to the perceptual world. In particular, we demonstrate a system, called OLAV, which is able, for the first time, to (1) learn to form discrete concepts from sensory data; (2) ground language (n-grams) to these concepts; (3) induce a grammar for the language being used to describe the perceptual world; and moreover to do all this incrementally, without storing all previous data. The learning is achieved in a loosely-supervised manner from raw linguistic and visual data. Moreover, the learnt model is transparent, rather than a black-box model and is thus open to human inspection. The visual data is collected using three different robotic platforms deployed in real-world and simulated environments and equipped with different sensing modalities, while the linguistic data is collected using online crowdsourcing tools and volunteers. The analysis performed on these robots demonstrates the effectiveness of the framework in learning visual concepts, language groundings and grammatical structure in these three online settings.
URL: https://eprints.whiterose.ac.uk/181078/
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