Abstract
A key problem in the learning of phonologies is contending with the interdependence of the mapping and the lexicon. This paper presents an learning algorithm combining an existing procedure for learning restrictive mappings (Biased Constraint Demotion) with inconsistency detection, and illustrates the algorithm using a system of both predictable and lexical stress grammars. The heart of the algorithm's strategy is to respond to the failure of a hypothesis by attempting to modify the mapping first, and only considering modifying the lexicon when altering the mapping proves inadequate. The construction of the mapping via Biased Constraint Demotion involves the accumulation of ranking arguments (winner/loser pairs) which make reference to hypothesized lexical entries. This creates a potential problem when the learner considers altering the lexical entries referred to by the ranking arguments. The proposed algorithm deals with this by altering the list of ranking arguments whenever the lexicon is changed, via a process termed "surgery", so that they accurately reflect the updated lexicon. This process allows the learner to more quickly determine if a proposed change to the lexicon will actually resolve the failure of the preceding hypothesis. Computer simulation results are provided to demonstrate the algorithm's efficiency.