. Employing neural network training paradigms in such domains can prove to be prohibitively expensive. An algorithm, originally proposed for supervised on-line learning, has been improvised upon to make it suitable for deployment in large volume, highdimensional domains. The basic strategy is to divide

In this paper, we propose a novel strategy which is designed to enhance the accuracy of the parser by simplifying complex sentences before parsing. This approach involves the separate parsing of the constituent sub-sentences within a complex sentence. To achieve that, the divide-and-conquer

demanding both in terms of arithmetic operations and in memory requirements. This paper discusses two divide and conquer strategies applied to LSI, with the goal of alleviating these difficulties. These strategies process a data set by dividing it in subsets and conquering the LSI results on each subset

In this paper we propose a divide and conquer strategy for the calculation of hazard free prime implicants. It is capable of calculating all hazard free prime implicants but it can be restricted to calculate only those primes that can possibly contribute to a solution. We show

Abstract. The goal of Process Mining is to extract process models from logs of a system. Among the possible models to represent a process, Petri nets is an ideal candidate due to its graphical representation, clear semantics and expressive power. The theory of regions can be used to transform a log into a Petri net, but unfortunately the transformation requires algorithms with high complexity. This paper provides techniques to overcome this limitation. Either by using decomposition techniques, or by clustering events in the log and working on projections, the proposed approach can be used to widen the applicability of classical region-based techniques. 1

This paper describes our participation of the Recognizing Textual Entailment challenge this year. Based on our promising results in the RTE-3 challenge last year (66.9 % of accuracy) using a precision-oriented puristic syntactic approach (puristic in the sense that we only performed dependency parsing), we explored further extensions of this perspective. By extension, we developed more specialized RTE-modules to tackle more cases (i.e., entailment pairs) while trying to keep high accuracy. 1

for Recognizing Textual Entailment For our participation in the RTE-3 challenge (Giampiccolo et al., 2007), we applied a puristic syntactic approach (puristic in the sense that we only performed dependency parsing), and got 66.9 % of accuracy, which ranked as one of the five best results (Wang and Neumann, 2007a). After that, we also

Abstract In this chapter, we discuss some approaches to the problem of protein structure prediction by addressing "simpler" sub-problems. The rationale behind this strategy is to develop methods for predicting some interesting structural characteristics of the protein, which can

We present a divide-and-conquer strategy based on finite state technology for shallow parsing of realworld German texts. In a first phase only the topological structure of a sentence (i.e., verb groups, subclauses) are determined. In a second phase the phrased grammars are applied to the contents

We present a divide-and-conquer strategy based on nite state technology for shallow parsing of real-world German texts. In a rst phase only the topo-logical structure of a sentence (i.e., verb groups, subclauses) are determined. In a second phase the phrasal grammars are applied to the contents