Publications

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  1. The compositionality of neural networks: integrating symbolism and connectionism

    Hupkes, D., Dankers, V., Mul, M., & Bruni, E. (preprint). The compositionality of neural networks: integrating symbolism and connectionism.

    arXiv BibTeX
  2. Incremental Reading for Question Answering

    Abnar, S., Bedrax-Weiss, T., Kwiatkowski, T., & Cohen, W. W. (2019). Incremental Reading for Question Answering. International Journal of Computational Linguistics and Applications.

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  3. Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains

    Abnar, S., Beinborn, L., Choenni, R., & Zuidema, W. (2019). Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains. BlackboxNLP, ACL 2019.

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  4. Robust Evaluation of Language-Brain Encoding Experiments

    Beinborn, L., Abnar, S., & Choenni, R. (2019). Robust Evaluation of Language-Brain Encoding Experiments. International Journal of Computational Linguistics and Applications.

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  5. Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment

    Jumelet, J., Zuidema, W., & Hupkes, D. (2019). Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment. ArXiv Preprint ArXiv:1909.08975.

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  6. Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning

    Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O’Donnell, T. J., Sainburg, T., & Gentner, T. Q. (2019). Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning. Topics in Cognitive Science. https://doi.org/10.1111/tops.12474

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  7. Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules?

    Alhama, R. G., & Zuidema, W. (2018). Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules? Journal of Artificial Intelligence Research, 61, 927–946.

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  8. Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information

    Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. (2018). Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. Proceedings EMNLP Workshop Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP).

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  9. Visualisation and ‘Diagnostic Classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure

    Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and ‘Diagnostic Classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61, 907–926.

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  10. Vector-space models of words and sentences

    Repplinger, M., Beinborn, L., & Zuidema, W. (2018). Vector-space models of words and sentences. Nieuw Archief Voor De Wiskunde.

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  11. The evolution of combinatorial structure in language

    Zuidema, W., & de Boer, B. (2018). The evolution of combinatorial structure in language. Current Opinion in Behavioral Sciences, 21, 138–144.

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  12. Selecting the model that best fits the data - Commentary on Tali Leibovich, Naama Katzin, Maayan Harel and Avishai Henik, From ‘sense of number’ to ‘sense of magnitude’ – The role of continuous magnitudes in numerical cognition (in press)

    van Woerkom, W., & Zuidema, W. (2017). Selecting the model that best fits the data - Commentary on Tali Leibovich, Naama Katzin, Maayan Harel and Avishai Henik, From ‘sense of number’ to ‘sense of magnitude’ – The role of continuous magnitudes in numerical cognition (in press). Behavioral and Brain Sciences, 40, e192.

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  13. The Forest Convolutional Network : Compositional Distributional Semantics with a Neural Chart and without Binarization

    Le, P., & Zuidema, W. (2015). The Forest Convolutional Network : Compositional Distributional Semantics with a Neural Chart and without Binarization. Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 1155–1164.

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  14. Five fundamental constraints on theories of the origins of music

    Merker, B., Morley, I., & Zuidema, W. (2015). Five fundamental constraints on theories of the origins of music. Philosophical Transactions of the Royal Society B: Biological Sciences, 370, 20140095.

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  15. Principles of structure building in music, language and animal song

    Rohrmeier, M., Zuidema, W., Wiggins, G. A., & Scharff, C. (2015). Principles of structure building in music, language and animal song. Philosophical Transactions of the Royal Society B: Biological Sciences, 370, 20140097.

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  16. Decomposing dendrophilia

    Honing, H., & Zuidema, W. (2014). Decomposing dendrophilia. Physics of Life Reviews, 11, 375–376.

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  17. The Inside-Outside Recursive Neural Network model for Dependency Parsing

    Le, P., & Zuidema, W. (2014). The Inside-Outside Recursive Neural Network model for Dependency Parsing. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 729–739.

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  18. Three Design Principles of Language: The Search for Parsimony in Redundancy

    Beekhuizen, B., Bod, R., & Zuidema, W. (2013). Three Design Principles of Language: The Search for Parsimony in Redundancy. Language and Speech, 56, 265–290.

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  19. Accurate parsing with compact tree-substitution grammars: Double-DOP

    Sangati, F., & Zuidema, W. (2011). Accurate parsing with compact tree-substitution grammars: Double-DOP. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 84–95.

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  20. What are the unique design features of language? Formal tools for comparative claims

    Zuidema, W., & Verhagen, A. (2010). What are the unique design features of language? Formal tools for comparative claims. Adaptive Behavior, 18, 48–65.

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  21. Children’s Grammars Grow More Abstract with Age-Evidence from an Automatic Procedure for Identifying the Productive Units of Language

    Borensztajn, G., Zuidema, W., & Bod, R. (2009). Children’s Grammars Grow More Abstract with Age-Evidence from an Automatic Procedure for Identifying the Productive Units of Language. Topics in Cognitive Science, 1, 175–188.

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  22. Models of Language Evolution : Does the Math Add Up ?

    de Boer, B., & Zuidema, W. (2009). Models of Language Evolution : Does the Math Add Up ? ILLC Preprint Series, 1–10.

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  23. Unsupervised methods for head assignments

    Sangati, F., & Zuidema, W. (2009). Unsupervised methods for head assignments. Proceedings of the European Chapter of the Association for Computational Linguistics, 701–709.

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  24. The evolution of combinatorial phonology

    Zuidema, W., & de Boer, B. (2009). The evolution of combinatorial phonology. Journal of Phonetics, 37, 125–144.

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  25. Taalverwerving: De wetenschap van de kleine wetenschapper

    Borensztajn, G., Zuidema, W., & Bod, R. (2008). Taalverwerving: De wetenschap van de kleine wetenschapper. Kunst En Wetenschap, 17, 9–10.

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  26. Evolutionary Explanations for Natural Language - Criteria from Evolutionary Biology

    Zuidema, W., & de Boer, B. (2008). Evolutionary Explanations for Natural Language - Criteria from Evolutionary Biology. ILLC Preprint Series.

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  27. Parsimonious Data-Oriented Parsing.

    Zuidema, W. (2007). Parsimonious Data-Oriented Parsing. Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 551–560.

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  28. Evolution: The erratic path towards complexity

    Barton, N., & Zuidema, W. (2003). Evolution: The erratic path towards complexity. Current Biology, 13, 649–651.

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  29. Is Evolvability Involved in the Origin of Modular Variation?

    Gardner, A., & Zuidema, W. (2003). Is Evolvability Involved in the Origin of Modular Variation? Evolution, 57, 1448–1450.

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  30. Evolution of an Optimal Lexicon under Constraints from Embodiment

    Zuidema, W., & Westermann, G. (2003). Evolution of an Optimal Lexicon under Constraints from Embodiment. Artificial Life, 9, 387–402.

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  31. How did we get from there to here in the evolution of language?

    Zuidema, W. H., & de Boer, B. (2003). How did we get from there to here in the evolution of language? Behavioral and Brain Sciences, 26, 694–707.

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  32. How the Poverty of the Stimulus Solves the Poverty of the Stimulus

    Zuidema, W. (2003). How the Poverty of the Stimulus Solves the Poverty of the Stimulus. Advances in Neural Information Processing Systems 15 (Proceedings NIPS’02), 15, 51.

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  33. The importance of social learning in the evolution of cooperation and communication - Commentary on Howard Rachlin, Altruism and Selfishness

    Zuidema, W. (2002). The importance of social learning in the evolution of cooperation and communication - Commentary on Howard Rachlin, Altruism and Selfishness. Behavioral and Brain Sciences, 25, 283–284.

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  1. Cosine Contours: a Multipurpose Representation for Melodies

    Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2021). Cosine Contours: a Multipurpose Representation for Melodies. Proceedings of the 22th International Conference on Music Information Retrieval. Presented at the Online. Online.

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  2. Musical Modes as Statistical Modes: Classifying Modi in Gregorian Chant

    Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2021). Musical Modes as Statistical Modes: Classifying Modi in Gregorian Chant. Proceedings of the 6th International Conference on Analytical Approaches to World Music. Presented at the Paris, France. Paris, France.

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  3. Mode Classification and Natural Units in Plainchant

    Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2020). Mode Classification and Natural Units in Plainchant. Proceedings of the 21th International Conference on Music Information Retrieval, 869–875. Montreal, Canada.

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  4. Studying Large Plainchant Corpora Using chant21

    Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2020). Studying Large Plainchant Corpora Using chant21. 7th International Conference on Digital Libraries for Musicology, 40–44. Montreal, Canada: ACM.

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  5. On the Realization of Compositionality in Neural Networks

    Baan, J., Leible, J., Nikolaus, M., Rau, D., Ulmer, D., Baumgärtner, T., … Bruni, E. (2019). On the Realization of Compositionality in Neural Networks. BlackboxNLP, ACL 2019.

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  6. Learning compositionally through attentive guidance

    Hupkes, D., Singh, A., Korrel, K., Kruszewski, G., & Bruni, E. (2019). Learning compositionally through attentive guidance. 20th International Conference on Computational Linguistics and Intelligent Text Processing.

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  7. The emergence of number and syntax units in LSTM language models

    Lakretz, Y., Kruszewski, G., Desbordes, T., Hupkes, D., Dehaene, S., & Baroni, M. (2019). The emergence of number and syntax units in LSTM language models. NAACL 2019.

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  8. The Fast and the Flexible: training neural networks to learn to follow instructions from small data

    Leonandya, R., Bruni, E., Hupkes, D., & Kruszewski, G. (2019). The Fast and the Flexible: training neural networks to learn to follow instructions from small data. The 13th International Conference on Computational Semantics (IWCS).

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  9. Assessing incrementality in sequence-to-sequence models

    Ulmer, D., Hupkes, D., & Bruni, E. (2019). Assessing incrementality in sequence-to-sequence models. Repl4NLP, ACL 2019.

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  10. Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity (preprint)

    Abnar, S., Ahmed, R., Mijnheer, M., & Zuidema, W. (2018). Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity (preprint). Proceedings Workshop on Cognitive Modeling and Computational Linguistics (CMCL). https://doi.org/10.3389/conf.fninf.2014.18.00084

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  11. Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information

    Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. (2018). Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. Proceedings EMNLP Workshop Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP).

    BibTeX
  12. Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue

    Hupkes, D., Bouwmeester, S., & Fernández, R. (2018). Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue. BlackboxNLP 2018, ACL.

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  13. Do language models understand anything? On the ability of LSTMs to understand negative polarity items

    Jumelet, J., & Hupkes, D. (2018). Do language models understand anything? On the ability of LSTMs to understand negative polarity items. BlackboxNLP 2018, ACL.

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  14. Segmentation as Retention and Recognition : the R & R model

    Alhama, R. G., & Zuidema, W. (2017). Segmentation as Retention and Recognition : the R & R model. Proceedings of the Conference of the Cognitive Science Society.

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  15. Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks

    Hupkes, D., & Zuidema, W. (2017). Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks. Workshop on Interpreting, Explaining and Visualizing Deep Learning (at NIPS).

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  16. Can Neural Networks learn Logical Reasoning?

    Veldhoen, S., & Zuidema, W. (2017). Can Neural Networks learn Logical Reasoning? Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML), pp. 35–41. University of Gothenburgh, Sweden.

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  17. Generalization in Artificial Language Learning: Modelling the Propensity to Generalize

    Alhama, R. G., & Zuidema, W. (2016). Generalization in Artificial Language Learning: Modelling the Propensity to Generalize. Proceeding Cognitive Aspects of Computational Language Learning (Workshop at ACL), 64–72.

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  18. POS-tagging of Historical Dutch

    Hupkes, D., & Bod, R. (2016). POS-tagging of Historical Dutch. Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC’16), 77–82.

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  19. Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs

    Le, P., & Zuidema, W. (2016). Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs. Workshop on Representation Learning (at ACL). https://doi.org/10.18653/v1/W16-1610

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  20. Diagnostic classifiers: revealing how neural networks process hierarchical structure

    Veldhoen, S., Hupkes, D., & Zuidema, W. (2016). Diagnostic classifiers: revealing how neural networks process hierarchical structure. Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (at NIPS).

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  21. Compositional Distributional Semantics with Long Short Term Memory

    Le, P., & Zuidema, W. (2015). Compositional Distributional Semantics with Long Short Term Memory. Proceedings *SEM. https://doi.org/10.18653/v1/S15-1002

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  22. Unsupervised Dependency Parsing: Let’s Use Supervised Parsers

    Le, P., & Zuidema, W. (2015). Unsupervised Dependency Parsing: Let’s Use Supervised Parsers. Proceedings NAACL.

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  23. The Forest Convolutional Network : Compositional Distributional Semantics with a Neural Chart and without Binarization

    Le, P., & Zuidema, W. (2015). The Forest Convolutional Network : Compositional Distributional Semantics with a Neural Chart and without Binarization. Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 1155–1164.

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  24. General purpose cognitive processing constraints and phonotactic propoerties of the vocabulary

    Monaghan, P., & Zuidema, W. H. (2015). General purpose cognitive processing constraints and phonotactic propoerties of the vocabulary. Proceedings of the International Conference of the Phonetic Sciences (ICPhS).

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  25. Rule Learning in Humans and Animals

    Alhama, R. G., Scha, R., & Zuidema, W. (2014). Rule Learning in Humans and Animals. Proceedings of the International Conference on the Evolution of Language, 371–372. World Scientific.

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  26. Inside-Outside Semantics: A Framework for Neural Models of Semantic Composition

    Le, P., & Zuidema, W. (2014). Inside-Outside Semantics: A Framework for Neural Models of Semantic Composition. Workshop on Deep Neural Networks and Representation Learning (at NIPS), 1–11.

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  27. The Inside-Outside Recursive Neural Network model for Dependency Parsing

    Le, P., & Zuidema, W. (2014). The Inside-Outside Recursive Neural Network model for Dependency Parsing. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 729–739.

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  28. Neural Networks, Algebraic Rules and Human Uniqueness

    Woensdregt, M. S., & Zuidema, W. (2014). Neural Networks, Algebraic Rules and Human Uniqueness. Proceedings of the International Conference on the Evolution of Language, 561–562. World Scientific.

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  29. Requirements on Scenarios for the Evolution of Language and Cognition

    Zuidema, W. (2014). Requirements on Scenarios for the Evolution of Language and Cognition. Proceedings of the International Conference on the Evolution of Language, 565–566. World Scientific.

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  30. Learning from errors: Using vector-based compositional semantics for parse reranking

    Le, P., Zuidema, W., & Scha, R. (2013). Learning from errors: Using vector-based compositional semantics for parse reranking. Proceedings Workshop on Continuous Vector Space Models and Their Compositionality (at ACL 2013).

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  31. Contextfreeness Revisited

    Zuidema, W. (2013). Contextfreeness Revisited. Proceedings of the Conference of the Cognitive Science Society, 1664–1669.

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  32. Learning Compositional Semantics for Open Domain Semantic Parsing

    Le, P., & Zuidema, W. (2012). Learning Compositional Semantics for Open Domain Semantic Parsing. Proceedings COLING, 1535–1552. Mumbai.

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  33. How should we Evaluate Models of Segmentation in Artificial Language Learning

    Alhama, R. G., & Scha, R. (2011). How should we Evaluate Models of Segmentation in Artificial Language Learning. Proceedings of the International Conference on Cognitive Modeling, 172–173.

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  34. Episodic grammar : a computational model of the interaction between episodic and semantic memory in language processing

    Borensztajn, G., & Zuidema, W. (2011). Episodic grammar : a computational model of the interaction between episodic and semantic memory in language processing. Proceedings of the Conference of the Cognitive Science Society, 507–512.

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  35. Adaptation in child directed speech: Evidence from corpora

    Kunert, R., Fernández, R., & Zuidema, W. (2011). Adaptation in child directed speech: Evidence from corpora. Proceedings of the Workshop on the Semantics and Pragmatics of Dialogue, 112–119.

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  36. Accurate parsing with compact tree-substitution grammars: Double-DOP

    Sangati, F., & Zuidema, W. (2011). Accurate parsing with compact tree-substitution grammars: Double-DOP. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 84–95.

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  37. Multi-agent simulations of the evolution of combinatorial phonology

    de Boer, B., & Zuidema, W. (2010). Multi-agent simulations of the evolution of combinatorial phonology. Proceedings of the Belgian/Netherlands Artificial Intelligence Conference, 18, 66–73.

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  38. Efficiently extract recurring tree fragments from large treebanks

    Sangati, F., Zuidema, W., & Bod, R. (2010). Efficiently extract recurring tree fragments from large treebanks. Proceedings LREC, 219–226.

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  39. The hierarchical prediction network: towards a neural theory of grammar acquisition

    Borensztajn, G., Zuidema, W., & Bod, R. (2009). The hierarchical prediction network: towards a neural theory of grammar acquisition. Proceedings of the Conference of the Cognitive Science Society, 2974–2979.

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  40. Thomas ’ theorem meets Bayes ’ rule : a model of the iterated learning of language

    Ferdinand, V., & Zuidema, W. (2009). Thomas ’ theorem meets Bayes ’ rule : a model of the iterated learning of language. Proceedings of the Conference of the Cognitive Science Society.

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  41. A generative re-ranking model for dependency parsing

    Sangati, F., Zuidema, W., & Bod, R. (2009). A generative re-ranking model for dependency parsing. Proceedings of the International Conference on Parsing Technologies (IWPT), 238–241.

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  42. Unsupervised methods for head assignments

    Sangati, F., & Zuidema, W. (2009). Unsupervised methods for head assignments. Proceedings of the European Chapter of the Association for Computational Linguistics, 701–709.

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  43. Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species

    van Heijningen, C. A. A., de Visser, J., Zuidema, W., & ten Cate, C. (2009). Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species. Proceedings of the National Academy of Sciences, 106, 20538–20543.

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  44. Communication, cooperation and coherence

    Sangati, F., & Zuidema, W. (2008). Communication, cooperation and coherence. In A. D. M. Smith, K. Smith, & R. Ferrer i Cancho (Eds.), Proceedings of the International Conference on the Evolution of Language. World Scientific.

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  45. A Gradual Path To Hierarchical Phrase-Structure: Insights from Modeling and Corpus-Data

    Zuidema, W. (2008). A Gradual Path To Hierarchical Phrase-Structure: Insights from Modeling and Corpus-Data. In A. D. M. Smith, K. Smith, & R. Ferrer i Cancho (Eds.), Proceedings of the International Conference on the Evolution of Language (pp. 509–510). World Scientific.

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  46. Parsimonious Data-Oriented Parsing.

    Zuidema, W. (2007). Parsimonious Data-Oriented Parsing. Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 551–560.

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  47. What are the productive units of natural language grammar?: a DOP approach to the automatic identification of constructions

    Zuidema, W. (2006). What are the productive units of natural language grammar?: a DOP approach to the automatic identification of constructions. Proceedings of the Conference on Computational Natural Language Learning (CoNLL), 29–36.

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  48. Theoretical Evaluation of Estimation Methods for Data-Oriented Parsing

    Zuidema, W. (2006). Theoretical Evaluation of Estimation Methods for Data-Oriented Parsing. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL), 1–4.

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  49. Beyond the argument from design

    Zuidema, W., & O’Donnell, T. J. (2006). Beyond the argument from design. Proceedings of the International Conference on the Evolution of Language. https://doi.org/10.1142/9789812774262

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  50. Optimal Communication in a Noisy and Heterogeneous Environment

    Zuidema, W. (2003). Optimal Communication in a Noisy and Heterogeneous Environment. Proceedings of the European Conference on Artificial Life, 651–658.

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  51. Grounding Formal Syntax in an Almost Real World

    Beule, J. D., van Looveren, J., & Zuidema, W. (2002). Grounding Formal Syntax in an Almost Real World. Proceedings of the Belgian/Netherlands Artificial Intelligence Conference.

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  52. Language adaptation helps language acquisition

    Zuidema, W. (2002). Language adaptation helps language acquisition. In B. Hallam, D. Floreano, J. Hallam, G. Hayes, & J.-A. Meye (Eds.), Proceedings of the International Conference on Simulation of Adaptive Behavior (Vol. 7, pp. 417–418). MIT Press.

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  53. Emergent syntax: The unremitting value of computational modeling for understanding the origins of complex language

    Zuidema, W. H. (2001). Emergent syntax: The unremitting value of computational modeling for understanding the origins of complex language. Proceedings of the European Conference on Artificial Life, 641–644.

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  54. Towards formal models of embodiment and self-organization of language

    Zuidema, W., & Westermann, G. (2001). Towards formal models of embodiment and self-organization of language. Proceedings of the Workshop on Developmental Embodied Cognition at the Annual Meeting of the Cognitive Science Society.

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  55. Selective advantages of syntactic language - a model study

    Zuidema, W., & Hogeberg, P. (2000). Selective advantages of syntactic language - a model study. Proceedings of the Annual Meeting of the Cognitive Science Society.

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  56. Social patterns guide evolving grammars

    Zuidema, W., & Hogeweg, P. (2000). Social patterns guide evolving grammars. Proceedings of the International Conference on the Evolution of Language.

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  1. Models of human language and speech processing

    Zuidema, W., & Fitz, H. (2019). Models of human language and speech processing. In P. Hagoort (Ed.), Human Language. MIT press.

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  2. Vector-based and Neural Models of Semantics

    Zuidema, W., & Le, P. (2019). Vector-based and Neural Models of Semantics. In P. Hagoort (Ed.), Human Language. MIT press.

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  3. Five fundamental constraints on theories of the origins of music

    Merker, B., Morley, I., & Zuidema, W. (2018). Five fundamental constraints on theories of the origins of music. In H. J. Honing (Ed.), The Origins of Musicality (pp. 49–80). MIT Press.

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  4. Formal Models of Structure Building in Music, Language, and Animal Song

    Zuidema, W., Hupkes, D., Wiggins, G. A., Scharff, C., & Rohrmeirer, M. (2018). Formal Models of Structure Building in Music, Language, and Animal Song. In H. J. Honing (Ed.), The Origins of Musicality (pp. 253–286). MIT Press.

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  5. Zwarte inktvlekjes op een witte achtergrond

    Zuidema, J. (2015). Zwarte inktvlekjes op een witte achtergrond. In M. Geels & T. van Opijnen (Eds.), Nederland in ideeën – Dit is het mooiste ooit. Maven.

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  6. Systematicity and the Need for Encapsulated Representations

    Borensztajn, G., Zuidema, W., & Bechtel, W. (2014). Systematicity and the Need for Encapsulated Representations. In P. Calvo & J. Symons (Eds.), The Architecture of Cognition (p. 165). MIT Press.

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  7. Relax – Taalfouten bestaan niet

    Zuidema, J. (2014). Relax – Taalfouten bestaan niet. In M. Geels & T. van Opijnen (Eds.), Nederland in ideeën – Dit wil je weten (pp. 256–258). Maven.

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  8. Analyzing the Structure of Bird Vocalizations and Language: Finding Common Ground

    ten Cate, C., Lachlan, R., & Zuidema, W. (2013). Analyzing the Structure of Bird Vocalizations and Language: Finding Common Ground. In J. J. Bolhuis & M. Everaert (Eds.), Birdsong, Speech, and Language (pp. 243–260). MIT Press.

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  9. Language in Nature: On the Evolutionary Roots of a Cultural Phenomenon

    Zuidema, W. (2013). Language in Nature: On the Evolutionary Roots of a Cultural Phenomenon. In B. Philippe & S. Kenny (Eds.), The Language Phenomenon (pp. 163–189). Springer.

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  10. Honderdduizend jaar nuttig geklets

    Zuidema, J. (2013). Honderdduizend jaar nuttig geklets. In M. Geels & T. van Opijnen (Eds.), Nederland in ideeën: 101 denkers over inzichten en innovaties die ons land verander(d)en. Maven.

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  11. Van A naar B

    Zuidema, J. (2013). Van A naar B. In A. Reuneker, R. Boogaart, & S. Lensink (Eds.), Aries netwerk – een constructicon. Columns aangeboden aan Arie Verhagen.

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  12. Modeling in the Language Sciences

    Zuidema, W., & De Boer, B. (2013). Modeling in the Language Sciences. In R. J. Podesva & D. Sharma (Eds.), Research Methods in Linguistics (pp. 422–439). Cambridge University Press.

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  13. Simulations Of Socio-Linguistic Change: Implications for Unidirectionality

    Versteegh, M., Sangati, F., & Zuidema, W. (2010). Simulations Of Socio-Linguistic Change: Implications for Unidirectionality. In Proceedings of the International Conference on the Evolution of Language (pp. 511–512). World Scientific.

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  1. Task-Based Semantic Evaluation of Parse Tree Discontinuity - Master’s thesis

    de Gooijer, S. (2016). Task-Based Semantic Evaluation of Parse Tree Discontinuity - Master’s thesis (Master's thesis).

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  2. An experiment in iterated function learning

    Ferdinand, V. (2008). An experiment in iterated function learning.

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  3. Language adapting to the brain: a study of a Bayesian iterated learning model

    Ferdinand, V., & Zuidema, W. (2008). Language adapting to the brain: a study of a Bayesian iterated learning model.

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  4. Bayesian Model Merging for Unsupervised Constituent Labeling and Grammar Induction

    Borensztajn, G., & Zuidema, W. (2007). Bayesian Model Merging for Unsupervised Constituent Labeling and Grammar Induction (pp. 1–9).

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  5. The Major Transitions in the Evolution of Language

    Zuidema, W. H. (2005). The Major Transitions in the Evolution of Language (PhD thesis). https://doi.org/10.1142/9789812774262_0065

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  6. On the Relevance of Language Evolution Models for Cognitive Science

    Zuidema, W., & Westermann, G. (2001). On the Relevance of Language Evolution Models for Cognitive Science.

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  7. Evolution of syntax in groups of agents - Master’s thesis

    Zuidema, W. H. (2000). Evolution of syntax in groups of agents - Master’s thesis (Master's thesis).

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