<OT> New Posting: ROA-746

[Rutgers Optimality Archive]

ROA 746-0605

Comparing two Optimality-Theoretic Learning Algorithms for Latin stress

Diana Apoussidou <d.apoussidou at uva.nl>
Paul Boersma <paul.boersma at uva.nl>

Direct link: http://roa.rutgers.edu/view.php3?roa=746


Abstract:
This paper compares the performance of two formal Optimality-Theoretic 
learning algorithms in modelling the acquisition of Latin stress from 
overt language data: Error-Driven Constraint Demotion (EDCD; Tesar 
1995) and the Gradual Learning Algorithm (GLA; Boersma 1997). We 
present computer simulations of learners who are trained on several 
kinds of overt Latin stress patterns: a case with main stress only, 
three cases with overtly available secondary stress, and a case in 
which the learners are free to invent their own secondary stress 
patterns. Several of these cases turn out to be learnable with the GLA, 
none with EDCD.

Comments:
Keywords: Latin, primary stress, secondary stress
Areas: Learnability, Language Acquisition
Type: Conference Proceedings Chapter

Direct link: http://roa.rutgers.edu/view.php3?roa=746