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.
Jarosz, Gaja. 2016. Investigating the Efficiency of Parsing Strategies for the Gradual Learning Algorithm. In Dimensions of Stress. Cambridge University Press.
Schrimpf, Natalie & Gaja Jarosz. 2014. Comparing Models of Phonotactics for Word Segmentation. Association of Computational Linguistics: Joint Meeting of SIGMORPHON and SIGFSM 2014.
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.
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.
Jarosz, Gaja. 2011. The Roles of Phonotactics and Frequency in the Learning of Alternations. In Proceedings of the 35th Annual Meeting of the Boston University Conference on Language Development.
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
Jarosz, Gaja 2009. Restrictiveness in Phonological Grammar and Lexicon Learning. In Proceedings of the 43rd Annual Meeting of the Chicago Lingusitic Society.
Jarosz, Gaja. 2007. Stages of Acquisition without Ranking Biases: the Roles of Frequency and Markedness in Phonological Learning. In M. Becker (ed.), UMass Occasional Papers in Linguistics.
Jarosz, Gaja. 2006. Rich Lexicons and Restrictive Grammars – Maximum Likelihood Learning in Optimality Theory. PhD dissertation, Johns Hopkins University. Rutgers Optimality Archive #884.
Jarosz, Gaja. 2006. Richness of the Base and Probabilistic Unsupervised Learning in Optimality Theory. Association for Computational Linguistics: Proceedings of the Eighth Meeting of the ACL Special Interest Group in Computational Phonology.