Recent work on loglinear models in probabilistic constraint logic programming is applied to firstorder probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities. We have a cons...

We investigate the influence of information status (IS) on constituent order in German, and integrate our findings into a loglinear surface realisation ranking model. We show that the distribution of pairs of IS categories is strongly asymmetric. Moreover, each category is correlated with morphosyntactic features, which can be automatically detected. We build a loglinear model that incorporates...

Portuguese dictionaries lack information about the kind of phrases or clauses a verb goes with. In this paper we describe how this information can be computationally learned from an automatically tagged corpus with almost 10,000,000 words. Loglinear modeling for categorical data will be used to analyze and automatically identify the subcatego-rization dependencies. Loglinear models were also us...

This article considers unbiased prediction of levels when data series are modeled as a random walk with drift and other exogenous factors after taking natural logs. We derive the unique predictors for growth its variance. Derivation level forecasts is more involved because last observation enters conditional expectation highly correlated parameter estimates, even asymptotically. leads to concep...