| ROA: | 544 |
|---|---|
| Title: | Learning constraint sub-hierarchies. The Bidirectional Gradual Learning Algorithm |
| Authors: | |
| Comment: | |
| Length: | 34 |
| Abstract: | The paper proposes a variant of Boersma's Gradual Learning Algorithm for Stochastic Optimality Theory. While in the original version the learner is always (or tries to become) a speaker, I assume that the learner is both speaker and hearer. This learning theory is applied to the OT system from Aissen (2000), which was developed to explain the typology of differential case marking. It can be shown that the constraint sub-hierarchies that Aissen assumes to be universal follow from the statistical patterns of language use that have been uncovered in several corpus studies, if one adopts the bidirectional learning approach. The paper finally reports some tentative considerations on the repercussions of this learning theory for typology and diachrony. |
| Type: | Paper/tech report |
| Area/Keywords: | Learnability,Semantics,Computation |
| Article: | Version 1 |