Výsledky vyhledávání - "Alhama, R.G."

How does linguistic context influence word learning?

Autor: Alhama, R.G., Rowland, C.F., Kidd, E.

Publikováno v: Journal of Child Language. Advance online publication
Journal of Child Language

Contains fulltext : 294424.pdf (Publisher’s version ) (Open Access) While there are well-known demonstrations that children can use distributional information to acquire multiple components of language, the underpinnings of these achievements are u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::097f6de308c82e318829ba997ce097d1
https://hdl.handle.net/21.11116/0000-000D-5044-C21.11116/0000-000D-5042-E

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Evaluating Word Embeddings for Language Acquisition

Autor: Alhama, R.G., Rowland, C.F., Kidd, E., Chersoni, E., Jacobs, C., Oseki, Y., Prévot, L., Santus, E.

Publikováno v: Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, 38-42
STARTPAGE=38;ENDPAGE=42;TITLE=Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Chersoni, E.; Jacobs, C.; Oseki, Y. (ed.), Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, 38-42. Stroudsburg, PA : Association for Computational Linguistics (ACL)
STARTPAGE=38;ENDPAGE=42;TITLE=Chersoni, E.; Jacobs, C.; Oseki, Y. (ed.), Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Chersoni, E.; Jacobs, C.; Oseki, Y. (ed.), Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pp. 38-42

Item does not contain fulltext Continuous vector word representations (or word embeddings) have shown success in capturing semantic relations between words, as evidenced by evaluation against behavioral data of adult performance on semantic tasks (Pe

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https://doi.org/10.18653/v1/2020.cmcl-1.4

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Segmentation as Retention and Recognition: the R&R model

Autor: Alhama, R.G., Zuidema, W., Gunzelmann, G., Howes, A., Tenbrink, T., Davelaar, E.J.

Publikováno v: CogSci 2017: proceedings of the 39th Annual Meeting of the Cognitive Science Society : London, UK : 26-29 July 2017 : Computational Foundations of Cognition, 2, 1531-1536

We present the Retention and Recognition model (R&R), a probabilistic exemplar model that accounts for segmentation in Artificial Language Learning experiments. We show that R&R provides an excellent fit to human responses in three segmentation exper

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https://dare.uva.nl/personal/pure/en/publications/segmentation-as-retention-and-recognition-the-rr-model(3b48d0f9-1575-4511-92f2-5c1b166ca4b1).html

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Generalization in Artificial Language Learning: Modelling the Propensity to Generalize

Autor: Alhama, R.G., Zuidema, W., Korhonen, A., Lenci, A., Murphy, B., Poibeau, T., Villavicencio, A.

Publikováno v: The 54th Annual Meeting of the Association for Computational Linguistics: proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning: August 11, 2016, Berlin, Germany, 64-72
STARTPAGE=64;ENDPAGE=72;TITLE=The 54th Annual Meeting of the Association for Computational Linguistics: proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning

Experiments in Artificial Language Learning have revealed much about the ability of human adults to generalize to novel grammatical instances (i.e., instances consistent with a familiarization pattern). Notably, generalization appears to be negativel

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https://dare.uva.nl/personal/pure/en/publications/generalization-in-artificial-language-learning-modelling-the-propensity-to-generalize(d51bd84d-7db5-4fd7-b720-98b2ef0afe8c).html

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Pre-Wiring and Pre-Training: What does a neural network need to learn truly general identity rules?

Autor: Alhama, R.G., Zuidema, W., Besold, T.R., Bordes, A., d'Avila Garcez, A., Wayne, G.

Publikováno v: Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016: co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016) : Barcelona, Spain, December 9, 2016
Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016

In an influential paper, Marcus et al. [1999] claimed that connectionist models cannot account for human success at learning tasks that involved generalization of abstract knowledge such as grammatical rules. This claim triggered a heated debate, cen

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https://dare.uva.nl/personal/pure/en/publications/prewiring-and-pretraining-what-does-a-neural-network-need-to-learn-truly-general-identity-rules(8e61e5f3-ced7-48e7-9573-49108d3b85d8).html

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How should we evaluate models of segmentation in artificial language learning?

Autor: Alhama, R.G., Scha, R., Zuidema, W., Taatgen, N.A., van Vugt, M.K., Borst, J.P., Mehlhorn, K.

Publikováno v: Proceedings of ICCM 2015: 13th International Conference on Cognitive Modeling : April 9-11, Groningen, The Netherlands, 172-173
STARTPAGE=172;ENDPAGE=173;TITLE=Proceedings of ICCM 2015

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https://dare.uva.nl/personal/pure/en/publications/how-should-we-evaluate-models-of-segmentation-in-artificial-language-learning(21c21a70-fc88-4ccf-aede-3bc074abc882).html

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Rule Learning in Humans and Animals

Autor: Alhama, R.G., Scha, R., Zuidema, W., Cartmill, E.A., Roberts, S., Lyn, H., Cornish, H.

Publikováno v: The Evolution of Language: proceedings of the 10th International Conference (EVOLANG10), Vienna, Austria, 14-17 April 2014, 371-372
STARTPAGE=371;ENDPAGE=372;TITLE=The Evolution of Language

In recent years, artificial language learning experiments have revealed a rich and complex picture of the abilities of different species and different human age groups to discover simple patterns in sequences. In one influential study, Aslin et al. (

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https://dare.uva.nl/personal/pure/en/publications/rule-learning-in-humans-and-animals(1c1413af-7dd9-41d6-86a1-f105a9d5be3a).html

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