Cognitive Limitations Impose Advantageous Constraints on Word Segmentation
Hitczenko, Kasia & Gaja Jarosz. 2015. Cognitive Limitations Impose Advantageous Constraints on Word Segmentation. In Proceedings of the 39th Annual Meeting of the Boston University Conference on Language Development.
Investigating the Efficiency of Parsing Strategies for the Gradual Learning Algorithm
Jarosz, Gaja. 2016. Investigating the Efficiency of Parsing Strategies for the Gradual Learning Algorithm. In Dimensions of Stress. Cambridge University Press.
Comparing Models of Phonotactics for Word Segmentation
Schrimpf, Natalie & Gaja Jarosz. 2014. Comparing Models of Phonotactics for Word Segmentation. Association of Computational Linguistics: Joint Meeting of SIGMORPHON and SIGFSM 2014.
Serial Markedness Reduction
Jarosz, Gaja. 2014. Serial Markedness Reduction. Proceedings of 2013 Annual Meetings on Phonology 1(1), Amherst, MA.
Naive Parameter Learning for Optimality Theory – the Hidden Structure Problem
Jarosz, Gaja. 2013. Naive Parameter Learning for Optimality Theory – the Hidden Structure Problem. In Proceedings of the 40th Annual Meeting of the North East Linguistic Society.
Learning with Hidden Structure in Optimality Theory and Harmonic Grammar: Beyond Robust Interpretive Parsing
Jarosz, Gaja. 2013. Learning with Hidden Structure in Optimality Theory and Harmonic Grammar: Beyond Robust Interpretive Parsing. In Phonology 30(1), 27-71. Cambridge University Press.
The Richness of Distributional Cues to Word Boundaries in Speech to Young Children
Jarosz, Gaja & J. Alex Johnson. 2013. The Richness of Distributional Cues to Word Boundaries in Speech to Young Children. In Language Learning and Development 9(2), 175-210.
Implicational Markedness and Frequency in Constraint-Based Computational Models of Phonological Learning
Jarosz, Gaja. 2010. Implicational Markedness and Frequency in Constraint-Based Computational Models of Phonological Learning. In Journal of Child Language 37(3), Special Issue on Computational models of child language learning, 565-606. Cambridge: Cambridge University Press. DOI: http://dx.doi.org/10.1017/S0305000910000103
