3 resultados para Alternative system


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254 p : il, graf. col.

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In the middle of the so-called pension crisis, the ageing of population and the sharp decrease in affiliations to Social Security are threatening the well-functioning of the Spanish public pension system. The purpose of this paper is to present the main challenges to be faced by the Spanish pension system, as well as to shed light on the main determinants that will condition the evolution of pension expenditure over GDP along the following decades. This quantitative analysis, which considers the 2011 and 2013 pension reforms, uses the latest data on the Spanish demographic, labor market and institutional factors in order to project the evolution of the system from the expenditure side up to 2060. For the purpose of analyzing the dynamics and the underlying drivers of pension expenditure, the so-called aggregate accounting approach will be utilized. The alternative scenarios proposed allow for exploring the robustness of the results within the baseline scenario, which constitutes the reference point of projections in the exercise. The analysis concludes that, by the end of the projected period, Spain will count on a similar public pension expenditure ratio as the current one.

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The main contribution of this work is to analyze and describe the state of the art performance as regards answer scoring systems from the SemEval- 2013 task, as well as to continue with the development of an answer scoring system (EHU-ALM) developed in the University of the Basque Country. On the overall this master thesis focuses on finding any possible configuration that lets improve the results in the SemEval dataset by using attribute engineering techniques in order to find optimal feature subsets, along with trying different hierarchical configurations in order to analyze its performance against the traditional one versus all approach. Altogether, throughout the work we propose two alternative strategies: on the one hand, to improve the EHU-ALM system without changing the architecture, and, on the other hand, to improve the system adapting it to an hierarchical con- figuration. To build such new models we describe and use distinct attribute engineering, data preprocessing, and machine learning techniques.