Merging the hypothetical extraction method and the classical multiplier approach: a hybrid possibility for identifying key distributive sectors

The two main alternative methods used to identify key sectors within the input-output approach, the Classical Multiplier method (CMM) and the Hypothetical Extraction method (HEM), are formally and empirically compared in this paper. Our findings indicate that the main distinction between the two approaches stems from the role of the internal effects. These internal effects are quantified under the CMM while under the HEM only external impacts are considered. In our comparison, we find, however that CMM backward measures are more influenced by within-block effects than the proposed forward indices under this approach. The conclusions of this comparison allow us to develop a hybrid proposal that combines these two existing approaches. This hybrid model has the advantage of making it possible to distinguish and disaggregate external effects from those that a purely internal. This proposal has also an additional interest in terms of policy implications. Indeed, the hybrid approach may provide useful information for the design of ''second best'' stimulus policies that aim at a more balanced perspective between overall economy-wide impacts and their sectoral distribution.

Towards the automatic merging of language resources

Language Resources are a critical component for Natural Language Processing applications. Throughout the years many resources were manually created for the same task, but with different granularity and coverage information. To create richer resources for a broad range of potential reuses, nformation from all resources has to be joined into one. The hight cost of comparing and merging different resources by hand has been a bottleneck for merging existing resources. With the objective of reducing human intervention, we present a new method for automating merging resources. We have addressed the merging of two verbs subcategorization frame (SCF) lexica for Spanish. The results achieved, a new lexicon with enriched information and conflicting information signalled, reinforce our idea that this approach can be applied for other task of NLP.

Towards the Fully Automatic Merging of Lexical Resources: A Step Forward

This article reports on the results of the research done towards the fully automatically merging of lexical resources. Our main goal is to show the generality of the proposed approach, which have been previously applied to merge Spanish Subcategorization Frames lexica. In this work we extend and apply the same technique to perform the merging of morphosyntactic lexica encoded in LMF. The experiments showed that the technique is general enough to obtain good results in these two different tasks which is an important step towards performing the merging of lexical resources fully automatically.

Towards the Automatic Merging of Lexical Resources: Automatic Mapping

Lexical Resources are a critical component for Natural Language Processing applications. However, the high cost of comparing and merging different resources has been a bottleneck to have richer resources with a broad range of potential uses for a significant number of languages.With the objective of reducing cost byeliminating human intervention, we present a new method for automating the merging of resources,with special emphasis in what we call the mapping step. This mapping step, which converts the resources into a common format that allows latter the merging, is usually performed with huge manual effort and thus makes the whole process very costly. Thus, we propose a method to perform this mapping fully automatically. To test our method, we have addressed the merging of two verb subcategorization frame lexica for Spanish, The resultsachieved, that almost replicate human work, demonstrate the feasibility of the approach.

A Method Towards the Fully Automatic Merging of Lexical Resources

Lexical Resources are a critical component for Natural Language Processing applications. However, the high cost of comparing and merging different resources has been a bottleneck to obtain richer resources and a broader range of potential uses for a significant number of languages. With the objective of reducing cost by eliminating human intervention, we present a new method towards the automatic merging of resources. This method includes both, the automatic mapping of resources involved to a common format and merging them, once in this format. This paper presents how we have addressed the merging of two verb subcategorization frame lexica for Spanish, but our method will be extended to cover other types of Lexical Resources. The achieved results, that almost replicate human work, demonstrate the feasibility of the approach.

Measuring risk aversion with lists: A new bias

Various experimental procedures aimed at measuring individual risk aversion involve alist of pairs of alternative prospects. We first study the widely used method by Holt andLaury (2002), for which we find that the removal of some items from the lists yields asystematic decrease in risk aversion. This bias is quite distinct from other confoundsthat have been previously observed in the use of the Holt and Laury method. It may berelated to empirical phenomena and theoretical developments where better prospectsincrease risk aversion. Nevertheless, we have also found that the more recent elicitationmethod due to Abdellaoui et al. (2011), also based on lists, does not display anystatistically significant bias when the corresponding items of the list are removed. Ourresults suggest that methods other than the popular Holt and Laury one may bepreferable for the measurement of risk aversion.

An Interdomain PKI Model Based on Trust Lists

The penetration of PKI technology in the market is moving slowly due to interoperability concerns. Main causes are not technical but political and social since there is no trust development model that appropriately deals with multidomain PKIs. We propose a new architecture that on one hand considers that trust is not an homogeneous property but tied to a particular relation, and on the other hand, trust management must be performed through specialized entities that can evaluate its risks and threads. The model is based on trust certificate lists that allows users to hold a personalized trust view without having to get involved in technical details. The model dynamically adapts tothe context changes thanks to a new certificate extension, we have called TrustProviderLink (TPL).