Abstract:
One of the central problems in language learning is ambiguity in the overt data available to the learner. An overt form (the audible portion of an utterance) may be ambiguous between more than one full linguistic analysis, and different languages may choose different analyses. In metrical stress, a three syllable word with main stress on the middle syllable is ambiguous between an iambic analysis (in which the first two syllables are grouped together into a foot) and a trochaic analysis (in which the final two syllables are grouped together into a foot). Because linguistic principles evaluate entire linguistic analyses (and not just overt forms), the learner must overcome this ambiguity in order to use the overt forms to learn the grammar of their native language. This talk will present results from a learning algorithm capable of learning from ambiguous data. This research uses the linguistic framework of Optimality Theory, so that the primary goal of the learner is to learn the language-specific ranking of the universal constraints. The learner overcomes ambiguity by temporarily considering more than one analysis of the ambiguous form, and then eliminating those analyses which are inconsistent with other data from the language. The simulation results demonstrate that this strategy can succeed without explosive growth in the number of grammatical hypotheses the learner has to maintain during the course of learning.

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