This seminar will examine work on language learnability within Optimality Theory. The focus will be on the formal structure of the learning problems, the nature of algorithms that have been proposed to solve them, and the role played by the structure of linguistic theory in those proposed solutions. The empirical focus will derive primarily from linguistic data (rather than, e.g., child language production data). The course will start with an overview of relevant concepts from learnability theory. The core theory behind the learning of Optimality Theoretic constraint rankings will then be examined. Major topics in learnability to be examined include structural ambiguity, restrictive distributions ("the subset problem"), and the learning of lexical representations. Particular attention will be paid to the learning of phonological underlying forms, as an instance of lexical/grammatical interaction in learning.

The majority of the grade will be based upon a term paper. A good paper topic would be a detailed analysis of the learnability problems posed by some interesting linguistic phenomenon, perhaps in some grammatical system of independent interest to the student; other types of paper topics may be proposed to the instructor for consideration.

Some background in phonology and Optimality Theory will be useful for this course. The course will NOT presume that students have extensive background in mathematics or computer science. NOTE: interested students from outside of linguistics are strongly encouraged to contact the instructor (tesar@rutgers.edu). If you aren't confident in your phonological background, but are interested in language learnability and willing to do some extra background reading, talk to me!

Some possible readings for the semester:

Apoussidou, Diana. 2006. On-line learning of underlying forms. Ms. University of Amsterdam.

Apoussidou, Diana, and Boersma, Paul. 2004. Comparing two Optimality- Theoretic learning algorithms for Latin stress. In Proceedings of the 23rd West Coast Conference on Formal Linguistics, eds. Benjamin Schmeiser, Vineeta Chand, Ann Kelleher and Angelo Rodriguez, 101-114. Somerville, MA: Cascadilla. ROA-746.

Boersma, Paul, and Hayes, Bruce. 2001. Empirical tests of the Gradual Learning Algorithm. Linguistic Inquiry 32:45-86.

Bradshaw, Gary, and Lienert, Marsha. 1991. The Invention of the Airplane. In Proceedings of the 13th Annual Conference of the Cognitive Science Society, 605-610.

Jarosz, Gaja. to appear. Restrictiveness in phonological grammar and lexicon learning. In Proceedings of the 43rd Annual Meeting of the Chicago Linguistics Society.

Merchant, Nazarré, and Tesar, Bruce. 2008. Learning underlying forms by searching restricted lexical subspaces. In Proceedings of the Forty- First Conference of the Chicago Linguistics Society (2005), vol. II: The Panels, 33-47. ROA-811.

Pater, Joe. 2005. Learning a stratified grammar. In Proceedings of the 29th Boston University Conference on Language Development, eds. Alejna Brugos, Manuella Clark-Cotton and Seungwan Ha. Somerville, MA: Cascadilla. ROA-739.

Prince, Alan, and Tesar, Bruce. 2004. Learning phonotactic distributions. In Constraints in Phonological Acquisition, eds. René Kager, Joe Pater and Wim Zonneveld, 245-291. Cambridge: Cambridge University Press.

Tesar, Bruce. 2004. Using inconsistency detection to overcome structural ambiguity [Spring]. Linguistic Inquiry 35:219-253.

Tesar, Bruce. 2006. Learning from paradigmatic information. In Proceedings of the 36th Meeting of the North East Linguistics Society, eds. Christopher Davis, Amy Rose Deal and Youri Zabbal, 619-638: GLSA. ROA-795.

Tesar, Bruce. to appear. Learning phonological grammars for output- driven maps. In Proceedings of the 39th Annual Meeting of the North East Linguistics Society, eds. Suzi Lima, Kevin Mullin and Brian Smith. Amherst, MA: GLSA. ROA-1013.