VORTRAG ******* Oesterreichisches Forschungsinstitut fuer Artificial Intelligence(OeFAI) Schottengasse 3, A-1010 Wien Tel.: +43-1-53361120, Fax: +43-1-5336112-77, Email: sec@oefai.at ------------------------------------------------------------------------- Adam Albright, M.A. UCLA Linguistics Department, Los Angeles A MINIMAL GENERALIZATION APPROACH TO RULE INDUCTION Numerous computational models of morphology have taken on the task of identifying morphemes and decomposing complex words into their constituent parts. Relatively fewer models have taken on the reverse task, of learning rules to compose novel complex forms. Before a model can combine morphemes to create new forms, it must learn two things: (1) the contexts in which the morphemes occur (their distribution), and (2) the rates at which they occur (their productivity). I present an inductive approach to learning the distribution and productivity of rules. The model starts by considering pairs of morphologically related forms (e.g., (present,past)), and comparing them to discover the rules that are needed to derive one form from the other. Comparing (jump,jump[t]) and (sip,sip[t]), the model posits a rule suffixing [t] after stems ending in [p]; comparing further with (kick,kick[t]), it posits a rule suffixing [t] after stems ending in non-coronal voiceless stops, and so on. This conservative strategy, which we refer to as "minimal generalization", can accurately discover the distribution of morphemes, because it never generalizes a process beyond the contexts in which it has been observed. In order to discover the productivity of rules, the model collects simple statistics about the reliability of rules in different environments. After describing the basic model, I discuss a common but neglected pattern of linguistic exceptions, in which a few exceptional forms take the "wrong" allomorph. For example, the English verb (burn,burnt) uses the [t] suffix, but in the context of the voiced sound [n], which should ordinarily take the [d] allomorph. I present an algorithm for identifying this type of exception and learning the "true" distribution of allomorphs, even in the presence of such exceptions. Zeit: Donnerstag, 18. April, 2002, 18:30 Uhr pktl. Ort: Oesterreichisches Forschungsinstitut fuer Artificial Intelligence Schottengasse 3, 1010 Wien. OESTERREICHISCHES FORSCHUNGSINSTITUT FUER ARTIFICIAL INTELLIGENCE o.Univ.-Prof.Ing.Dr. Robert Trappl