Abstract
This paper deals with the interaction between two problems that arise in human language learning, structural ambiguity and the subset problem. The main claim of this paper is that the notion of r-measure, already proposed as a measure of grammatical restrictiveness, can be used to deal with complexities in structural ambiguity that result from interactions with subset learning. The approach combines an algorithm for contending with structural ambiguity, the Inconsistency Detection Learner, with an algorithm for dealing with the subset problem, Biased Constraint Demotion. Biased Constraint Demotion is designed to find, for a set of data, the grammar with the best r-measure, a measure of grammatical restrictiveness based upon a preference for markedness constraints dominating faithfulness constraints. The Inconsistency Detection Learner component tries different combinations of interpretations of structurally ambiguous forms, keeping only those combinations that are consistent with at least one grammar. For each such combination of interpretations, Biased Constraint Demotion is used to find the most restrictive grammar consistent with the interpretations. The different grammars are then compared with respect to their r-measures, and the grammar with the best r-measure is chosen by the learner as the final learned grammar. Computer simulation results, running the algorithm on an example exhibiting interaction between structural ambiguity and the subset problem, are presented.