Abstract
Paraphrase detection can be seen as the task of aligning sentences that convey the same information but yet are written in different forms. Such resources are important to automatically learn text-to-text rewriting rules. In this paper, we present a new metric for unsupervised detection of paraphrases and apply it in the context of clustering of paraphrases. An exhaustive evaluation is conducted over a set of standard paraphrase corpora and real-world web news stories (WNS) corpora. The results are promising as they outperform state-of-the-art measures developed for similar tasks. Copyright

Abstract

Abstract

Abstract
The object of this study is to consider how one could enable two or more Prolog-like systems to talk about problem solutions, and how more complex modes of communication could be defined in this framework. A 'reference list' associated with each clause indicates to which system the clause belongs (and where it came from). If a reference list is associated with an expression, it determines where this expression is to be sent if the appropriate action is taken. A special metapredicate is used to associate the answer (or answers) directly with each problem solved, and so, failures, for example, are represented in an explicit manner. The metapredicates sent to other systems may include the existing metapredicates of Prolog, the new metapredicates introduced, or conjunctions and disjunctions of metapredicates.

Abstract
This paper aims at evaluate the impact of imitation networks on organizations' survival rates within a Portuguese industrial cluster. We used a Multi-Agent framework to represent the industrial cluster, its firms and the rules underlying the imitation strategies. Several experiments were based on the density dependence model, where vital rates are related with the size of the population (population density). We have concluded that imitation seems to improve the vital dynamics of the population and that present information about a firm is enough to establish an imitation network.

Abstract
This paper concerns the problem of predicting the relative performance of classification algorithms. Our approach requires that experiments are conducted on small samples. The information gathered is used to identify the nearest learning curve for which the sampling procedure was fully carried out. This allows the generation of a prediction regarding the relative performance of the algorithms. The method automatically establishes how many samples are needed and their sizes. This is done iteratively by taking into account the results of all previous experiments - both on other datasets and on the new dataset obtained so far. Experimental evaluation has shown that the method achieves better performance than previous approaches.