Features in optimality theory: A computational model

Abstract

This dissertation presents a computational model of Optimality Theory (OT) (Prince and Smolensky 1993). The model provides an efficient solution to the problem of candidate generation and evaluation, and is demonstrated for the realm of phonological features. Explicit object-oriented implementations are proposed for autosegmental representations (Goldsmith 1976 and many others) and violable OT constraints and Gen operations on autosegmental representations. Previous computational models of OT (Ellison 1995, Tesar 1995, Eisner 1997, Hammond 1997, Karttunen 1998) have not dealt in depth with autosegmental representations. The proposed model provides a full treatment of autosegmental representations and constraints on autosegmental representations (Akinlabi 1996, Archangeli and Pulleyblank 1994, Ito, Mester, and Padgett 1995, Kirchner 1993, Padgett 1995, Pulleyblank 1993, 1996, 1998). Implementing Gen, the candidate generation component of OT, is a seemingly intractable problem. Gen in principle performs unlimited insertion; therefore, it may produce an infinite candidate set. For autosegmental representations, however, it is not necessary to think of Gen as infinite. The Obligatory Contour Principle (Leben 1973, McCarthy 1979, 1986) restricts the number of tokens of any one feature type in a single representation; hence, Gen for autosegmental features is finite. However, a finite Gen may produce a candidate set of exponential size. Consider an input representation with four anchors for each of five features: there are (2⁴ + 1)⁵, more than one million, candidates for such an input. The proposed model implements a method for significantly reducing the exponential size of the candidate set. Instead of first creating all candidates (Gen) and then evaluating them against the constraint hierarchy (Eval), candidate creation and evaluation are interleaved (cf. Eisner 1997, Hammond 1997) in a Gen-Eval loop. At each pass through the Gen-Eval loop, Gen operations apply to create the minimal number of candidates needed for constraint evaluation; this candidate set is evaluated and culled, and the set of Gen operations is reduced. The loop continues until the hierarchy is exhausted; the remaining candidate(s) are optimal. In providing explicit implementations of autosegmental representations, constraints, and Gen operations, the model provides a coherent view of autosegmental theory, Optimality Theory, and the interaction between the two.