The verb slot in causative constructions. Finding the best fit

Using the technique of multiple distinctive collexeme analysis, this paper seeks to determine the verbs that are distinctively associated with the non-finite verb slot of English periphrastic causative constructions. Not only does the analysis reveal that the various causative constructions are attracted to essentially different verbs, but by examining how these verbs fall into semantic classes, it also hints at subtle differences in meaning between the constructions. In addition, the paper shows how the technique of multiple distinctive collexeme analysis can be usefully combined with other, complementary methods, and briefly discusses a number of factors which influence the results of multiple distinctive collexeme analysis and should therefore ideally be taken into account.

Algorithms For Automatic And Robust Registration Of 3D Head Scans

wo methods for registering laser-scans of human heads and transforming them to a new semantically consistent topology defined by a user-provided template mesh are described. Both algorithms are stated within the Iterative Closest Point framework. The first method is based on finding landmark correspondences by iteratively registering the vicinity of a landmark with a re-weighted error function. Thin-plate spline interpolation is then used to deform the template mesh and finally the scan is resampled in the topology of the deformed template. The second algorithm employs a morphable shape model, which can be computed from a database of laser-scans using the first algorithm. It directly optimizes pose and shape of the morphable model. The use of the algorithm with PCA mixture models, where the shape is split up into regions each described by an individual subspace, is addressed. Mixture models require either blending or regularization strategies, both of which are described in detail. For both algorithms, strategies for filling in missing geometry for incomplete laser-scans are described. While an interpolation-based approach can be used to fill in small or smooth regions, the model-driven algorithm is capable of fitting a plausible complete head mesh to arbitrarily small geometry, which is known as "shape completion". The importance of regularization in the case of extreme shape completion is shown.