In this paper, we discuss an applic ation of Maximum Entropy to modeling the acquisition of subject and object processing in Italian. The model is able to learn from corpus data a set of experimentally and theoretically well-motivated linguistic constraints, as well as their relative salience in Italian grammar development and processing. The model is also shown to acquire robust syntactic generalizations by relying on the evidence provided by a small number of high token frequency verbs only. These results are consistent with current research focusing on the role of high frequency verbs in allowing children to converge on the most salient constraints in the grammar.
Climbing the Path to Grammar: a Maximum Entropy Model of Subject/Object Learning
LENCI, ALESSANDRO;
2005-01-01
Abstract
In this paper, we discuss an applic ation of Maximum Entropy to modeling the acquisition of subject and object processing in Italian. The model is able to learn from corpus data a set of experimentally and theoretically well-motivated linguistic constraints, as well as their relative salience in Italian grammar development and processing. The model is also shown to acquire robust syntactic generalizations by relying on the evidence provided by a small number of high token frequency verbs only. These results are consistent with current research focusing on the role of high frequency verbs in allowing children to converge on the most salient constraints in the grammar.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
