In this paper, we describe a grammatical formalism, called DepPattern, to write DepPattern distinguishes between open-choice and idiomatic rules. A grammar is defined as a set of lexical-syntactic rules at different levels of abstraction. In addition, a compiler was implemented so as to generate deterministic and robust parsers from DepPattern grammars. These parsers identify dependencies which can be used to improve corpus-based applications such as information extraction. At the end of this article, we describe an experiment which evaluates the efficiency of a dependency parser generated from a simple DepPattern grammar. In particular, we evaluated the precision of a semantic extraction method making use of a DepPattern-based parser.

The vertical jump height is commonly employed to assess indirectly the lower body strength and power. Traditional methods to assess the vertical jump height are been replaced by new emerging technologies as optical mat platform. The aim of the present study was checked the agreement between one traditional contact mat (Globus Ergo Tester) and an optical mat (Optojump System), and to investigate the interchangeability of this 2 commercial systems estimating vertical jump in different types of jump (Squat Jump, Counter Movement Jump and Abalakov). Significantly differences between methods in each jump condition were reported, high Inter-class Correlation Coefficients values (ranged between 0.972 to 0.990) were found between methods in each jump condition and the coefficient of variation values were ranged from 6.18 to 7.32. T-test revealed significantly differences between the limits of agreement at 95% in all jumps between methods jump heights. The results of this study show that the Optojump, as optical mat, reported lower values than the Globus Ergo Tester, a contact mat. There are evidences that Optojump and Globus Ergo Tester are not interchangeably.Key words: flight time; vertical jump; contact mat platform; optical mat platform. A b s t r a c tCorrespondence/correspondencia: Alejandro Santos-Lozano Department of Biomedical Sciences,University of León. España Email: asanl@unileon.es La altura de salto vertical es empleada para evaluar la potencia y fuerza del tren inferior. Los métodos tradicionales para evaluar el salto vertical están siendo sustituidos por nuevas tecnologías emergentes como las plataformas ópticas. El objetivo del presente estudio fue comprobar el grado de concordancia entre una plataforma de contacto (Globus Ergo Tester) y una óptica (Optojump System), e investigar si pueden ser utilizadas de manera intercambiable estimando distintos tipos de salto vertical (Squat Jump, Counter Movement Jump y Abalakov). Los resultados mostraron diferencias significativas entre los métodos estimando altura de salto vertical, un elevado valor de Coeficiente de Correlación (entre 0.972-0.990) y un Coeficiente de Variación comprendido entre 6.18 y 7.32. Las pruebas T revelaron diferencias significativas entre los límites de concordancia al 95% en todos los saltos entre plataformas. Los resultados del presente estudio muestran como el sistema óptico, Optojump, estimó valores más bajos que la plataforma por contacto, Globus Ergo Tester. Existen evidencias por tanto que estos sistemas no pueden ser utilizados intercambiablemente.Palabras clave: tiempo de vuelo, salto vertical, plataformas de contacto, plataformas ópticas. R e s u m e n

The objective of this article is to present an automatic tool for detecting and classifying grammatical errors in written language as well as to describe the evaluation protocol we have carried out to measure its performance on learner corpora. The tool was designed to detect and analyse the linguistic errors found in text essays, assess the writing proficiency, and propose solutions with the aim of improving the linguistic skills of students. It makes use of natural language processing and knowledge-rich linguistic resources. So far, the tool has been implemented for the Galician language. The system has been evaluated on two learner corpora reaching 91% precision and 65% recall (76% F-score) for the task of detecting different types of grammatical errors, including spelling, lexical and syntactic ones.

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