[dissertation work - used as CS job talk and elsewhere]

      Learning Natural-Language Grammars using a Bayesian Prior

  Language is a sea of uncertainty.  Over the past decade, computational
  linguistics has been learning to navigate this sea by means of
  probabilities.  Given a newspaper sentence that has hundreds of
  possible parses, for example, recent systems have set their course for
  the most probable parse.  Defining the most probable parse requires
  external knowledge about the relative probabilities of parse
  fragments: a kind of soft grammar.

  But how could one LEARN such a grammar?  This is a higher-level
  navigation problem -- through the sea of possible soft grammars.  I
  will present a clean Bayesian probability model that steers toward the
  most probable grammar.  It is guided by (1) a prior belief that much
  of a natural-language grammar tends to be predictable from other parts
  of the grammar, and (2) some evidence about the phenomena of the
  specific language, as might be available from previous parsing
  attempts or small hand-built databases.


[used as linguistics job talk and elsewhere]

		       Doing OT in a Straitjacket

  A universal theory of human phonology should be clearly specified
  and falsifiable.  To turn Optimality Theory (OT) into a complete
  proposal for phonological Universal Grammar, one must put some cards
  on the table: What kinds of constraints may an OT grammar state?
  And how can anyone tell what data this grammar predicts, without
  constructing infinite tableaux?

  In this talk I'll motivate a restrictive formalization of OT that
  allows just two types of simple, local constraint.  Gen freely
  proposes gestures and prosodic constituents; the constraints try to
  force these to coincide or not coincide temporally.  An efficient
  algorithm exists to find the optimal candidate.

  I will argue that despite its simplicity, primitive OT is expressive
  enough to describe and unify nearly all current work in OT
  phonology.  However, it is provably more constrained: because it is
  unable to mimic deeply non-local mechanisms like Generalized
  Alignment, it forces a new and arguably better account of metrical
  stress typology.  I will even discuss a proposal for constraining it
  further.