Semantic transfer in Verbmobil

This paper is a detailed discussion of semantic transfer in the context of the Verbmobil Machine Translation project. The use of semantic transfer as a translation mechanism is introduced and justified by comparison with alternative approaches. Some criteria for evaluation of transfer frameworks are discussed and a comparison is made of three different approaches to the representation of translation rules or equivalences. This is followed by a discussion of control of application of transfer rules and interaction with a domain description and inference component

The effect of pre-exposure on family resemblance categorization for stimuli of varying levels of perceptual difficulty

This study investigated the effect that pre-exposure to a set of stimuli has on the prevalence of family resemblance categorization. 64 participants were tested to examine the effect that pre-exposure type (same-stimuli vs unrelated-stimuli) and the perceptual difficulty of the stimuli (perceptually similar vs perceptually different) has on categorization strategy. There was a significant effect of perceptual difficulty, indicating that perceptually different stimuli evoked a higher level of family resemblance sorting than perceptually similar stimuli. There was no significant main effect of pre-exposure type; however, there was a significant interaction between pre-exposure type and level of perceptual difficulty. Post-hoc tests revealed that this interaction was the result of an increase in family resemblance sorting for the perceptually different stimuli under relevant preexposure but no such effect for perceptually similar stimuli. The theoretical implications of these findings are discussed

Functional Distributional Semantics

Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets