This paper introduces Stochastic Definite Clause Grammars, a stochastic variant of the wellknown Definite Clause Grammars. The grammar formalism supports parameter learning from annotated or unannotated corpora and provides a mechanism for parse selection by means of statistical inference. Unlike probabilistic contextfree grammars, it is a context-sensitive grammar formalism and it has the abil...

Kernel methods are widely used in statistical learning techniques. We recently introduced a general kernel framework based on weighted transducers or rational relations, rational kernels, to extend kernel methods to the analysis of variable-length sequences or more generally weighted automata. These kernels are efficient to compute and have been successfully used in applications such as spoken-...

Many information retrieval (IR) metrics are top-heavy, and some even have parameters for adjusting their discount curve. By choosing the right metric and parameters, the experimenter can arrive at a discount curve that is appropriate for their setting. However, in many cases changing the discount curve may not change the outcome of an experiment. This poster considers query-level directional ag...

We present an empirical analysis of the effect that the gain and discount functions have in the correlation between DCG and user satisfaction. Through a large user study we estimate the relationship between satisfaction and the effectiveness computed with a test collection. In particular, we estimate the probabilities that users find a system satisfactory given a DCG score, and that they agree ...

We consider the problem of digitalizing Euclidean line segments from R to Z. Christ et al. (DCG, 2012) showed how to construct a set of consistent digital segment (CDS) for d = 2: a collection of segments connecting any two points in Z2 that satisfies the natural extension of the Euclidean axioms to Z. In this paper we study the construction of CDSs in higher dimensions. We show that any total ...

In [4] we studied a new type of DCGs, Datalog grammars, which are inspired on database theory. Their efficiency was shown to be better than that of their DCG counterparts under (terminating) OLDTresolution. In this article we motivate a variant of Datalog grammars which allows us a meta-grammatical treatment of coordination. This treatment improves in some respects over previous work on coordin...

Starting in the mid-1980s, grammars in the DCG, PATR and LFG formats have been implemented at the University of Constance. Experience has shown that translating a grammar specification into machine interrretable code, e.g., Prolog, is difficult and errorprone. A current attempt to let students and research co-workers formulate and test grammar sketches in an abstract formalism directly on the c...

In one way or another, the extension of the standard Brownian motion process {B¡: t e [0,oo)} to a (Gaussian) random field {Bt: t € R+} involves a proof of the positive semi-definiteness of the kernel used to generalize p(s, 1) = cov(Bs,B¡) = s A t to multidimensional time. Simple direct analytical proofs are provided here for the cases of (i) the Levy multiparameter Brownian motion, (ii) the C...