| A review of computational models of basic rule learning: The neural-symbolic debate and beyond RG Alhama, W Zuidema Psychonomic bulletin & review 26, 1174-1194, 2019 | 22 | 2019 |
| Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules? RG Alhama, W Zuidema Journal of Artificial Intelligence Research 61, 927-946, 2018 | 22 | 2018 |
| When the “Tabula” is anything but “Rasa:” What determines performance in the auditory statistical learning task? A Elazar, RG Alhama, L Bogaerts, N Siegelman, C Baus, R Frost Cognitive Science 46 (2), e13102, 2022 | 21 | 2022 |
| Neural discontinuous constituency parsing M Stanojevic, RG Alhama 2017 Conference on Empirical Methods in Natural Language Processing, 1666-1676, 2017 | 16 | 2017 |
| Five ways in which computational modeling can help advance cognitive science: Lessons from artificial grammar learning W Zuidema, RM French, RG Alhama, K Ellis, TJ O'Donnell, T Sainburg, ... Topics in cognitive science 12 (3), 925-941, 2020 | 13 | 2020 |
| The role of information in visual word recognition: A perceptually-constrained connectionist account RG Alhama, N Siegelman, R Frost, BC Armstrong The 41st annual meeting of the cognitive science society (cogsci 2019), 83-89, 2019 | 6 | 2019 |
| How should we evaluate models of segmentation in artificial language learning? RG Alhama, R Scha, W Zudema University of Groningen, 2015 | 6 | 2015 |
| Evaluating word embeddings for language acquisition RG Alhama, CF Rowland, E Kidd (Online) Workshop on Cognitive Modeling and Computational Linguistics (CMCL …, 2020 | 5 | 2020 |
| Segmentation as Retention and Recognition: the R&R model RG Alhama, W Zuidema Proceedings of the 39th Annual Conference of the Cognitive Science Society., 2017 | 4 | 2017 |
| Computational modelling of artificial language learning: Retention, recognition & recurrence RG Alhama University of Amsterdam, 2017 | 3 | 2017 |
| Generalization in Artificial Language Learning: Modelling the Propensity to Generalize RG Alhama, W Zuidema Proceedings of the 7th Workshop on Cognitive Aspects of Computational …, 2016 | 3 | 2016 |
| Los avances tecnológicos y la ciencia del lenguaje M Martí, RG Alhama, M Recasens Universidad de Santiago de Compostela, 2012 | 3 | 2012 |
| Word Segmentation as Unsupervised Constituency Parsing RG Alhama Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 1 | 2022 |
| Retrodiction as delayed recurrence: the case of adjectives in italian and english RG Alhama, F Zermiani, A Khaliq Proceedings of the The 19th Annual Workshop of the Australasian Language …, 2021 | 1 | 2021 |
| Rule learning in humans and animals RG Alhama, R Scha, W Zuidema Proceedings of the international conference on the evolution of language …, 2014 | 1 | 2014 |
| Linguistic Productivity: the Case of Determiners in English RG Alhama, R Foushee, D Byrne, A Ettinger, S Goldin-Meadow, ... Proceedings of the 13th International Joint Conference on Natural Language …, 2023 | | 2023 |
| How does linguistic context influence word learning? RG Alhama, CF Rowland, K Evan Journal of Child Language 50 (6), 1374-1393, 2023 | | 2023 |
| Preface: Computational Linguistics in the Netherlands Journal E Vanmassenhove, M De Sisto, RG Alhama, TO Lentz, J Engelen, ... Computational Linguistics in the Netherlands Journal 12, 3-5, 2022 | | 2022 |
| How much context is helpful for noun and verb acquisition? RG Alhama, C Rowland, E Kidd International Conference on Cognitive Modelling, 2021 | | 2021 |
| 'Long nose’and ‘naso lungo’: Establishing the need for retrodiction in computational models of word learning F Zermiani, A Khaliq, RG Alhama Many Paths to Language, 2020 | | 2020 |
Willem ZuidemaAssociate Professor of NLP, Explainable AI and Cognitive Modelling, University of AmsterdamVerified email at uva.nl
