Agreement measures have been widely used in computational linguistics for more than 15 years to check the reliability of annotation processes. Although considerable effort has been made concerning categorization, fewer studies address unitizing, and when both paradigms are combined even fewer methods are available and discussed. The aim of this article is threefold. First, we advocate that to deal with unitizing, alignment and agreement measures should be considered as a unified process, because a relevant measure should rely on an alignment of the units from different annotators, and this alignment should be computed according to the principles of the measure. Second, we propose the new versatile measure γ, which fulfills this requirement and copes with both paradigms, and we introduce its implementation. Third, we show that this new method performs as well as, or even better than, other more specialized methods devoted to categorization or segmentation, while combining the two paradigms at the same time.

As (Lee et al., 2012) have shown, weighted constraint satisfaction problems can benefit from the introduction of global cost functions, leading to a new Cost Function Programming paradigm. In this paper, we explore the possibility of decomposing global cost functions in such a way that enforcing soft local consistencies on the decomposition offers guarantees on the level of consistency enforced on the original global cost function. We show that directional arc consistency and virtual arc consistency offer such guarantees. We conclude by experiments on decomposable cost functions showing that decompositions may be very useful to easily integrate efficient global cost functions in solvers.

A soft version of the well-known global constraint AllDifferent has been recently introduced for the Max-CSP framework ([9, 15, 16]). In this paper, we propose to soften AllDifferent in the Weighted CSP framework that is more general. We extend the two semantics of violation proposed in [9]: the first one is based on variables and the second one on the decomposition into a set of binary constraints of difference. For the first semantic, we propose a polynomial algorithm which maintains hyper- arc consistency. For the second one, we prove that checking hyper-arc consistency is an NP-Hard problem. So, we propose to maintain a local consistency using a filtering based on lower bounds computation. Finally, we present some experimental results and draw a few perspectives.