| ROA: | 426 |
|---|---|
| Title: | Using Inconsistency Detection to Overcome Structural Ambiguity in Language Learning |
| Authors: | Bruce Tesar |
| Comment: | |
| Length: | 41 |
| Abstract: | Using Inconsistency Detection to Overcome Structural Ambiguity in Language Learning Bruce Tesar Rutgers University This paper proposes the Inconsistency Detection Learner (IDL), an algorithm for language acquisition intended to address the problem of structural ambiguity. An overt, acoustically audible form is structurally ambiguous if different languages admitting the overt form would assign it different linguistic structural analyses. Because the learner has to be capable of learning any possible human language, and because the learner is dependent on overt data to determine what the target language is, the learner must be capable ultimately of inferring which analysis of an ambiguous overt form is correct by reference to other overt data of the language. IDL does this in a particularly direct way, by attempting to construct hypothesis grammars for combinations of interpretations of the overt forms, and discarding those combinations that are shown to be inconsistent. A specific implementation of IDL is given, based on Optimality Theory. Results are presented from a computational experiment in which this implementation of IDL was applied to all possible languages predicted by an Optimality theoretic system of metrical stress grammars. The experimental results show that this learning algorithm learns quite efficiently for languages from this system, completely avoiding the potential combinatoric growth in combinations of interpretations, and suggesting that this approach may play an important role in the acquisition mechanisms of human learners. |
| Type: | Paper/tech report |
| Area/Keywords: | Learnability |
| Article: | Version 1 |