![]() ![]() Sobre la base de este modelo, identificará luego nuevos términos en nuevos textos. Con estos ejemplos, el algoritmo elabora un modelo de los términos teniendo en cuenta la frecuencia de elementos léxicos, morfológicos y sintácticos en relación al vocabulario no terminológico. Este algoritmo aprende automáticamente a identificar términos durante una fase de entrenamiento en que se utilizan conjuntos de ejemplos de unidades terminológicas y no terminológicas. El modelo requiere escasa complejidad teórica y computacional, y no necesita recurrir a fuentes de conocimiento lingüístico u ontológico. Este método se utiliza para la identificación automática de los términos en los textos, y su efectividad es evaluada en este artículo mediante un estudio empírico en el caso de la terminología médica en inglés. En contraste con la tendencia general en terminología aplicada, que suele ser específica de una lengua y un dominio de especialidad, el presente artículo adopta unos principios generales acerca de las propiedades estadísticas de la terminología especializada y un método para obtener los parámetros correspondientes a una lengua en particular. The comparative evaluation shows that performance is significantly higher than other well-known systems.Este artículo presenta argumentos en favor de una aproximación estadística a la extracción de terminología, general a todas las lenguas pero con parámetros específicos para cada una de ellas. The model is then used for the later identification of new terminology in previously unseen text. With these examples, the algorithm creates a model of the terminology that accounts for the frequency of lexical, morphological and syntactic elements of the terms in relation to the non-terminological vocabulary. The algorithm learns to identify terms during a training phase where it is shown examples of both terminological and non-terminological units. The proposal is theoretically and computationally simple and disregards resources such as linguistic or ontological knowledge. This method is used for the automatic identification of terminology and is quantitatively evaluated in an empirical study of English medical terms. In contrast to many application-oriented terminology studies, which are focused on a particular language and domain, this paper adopts some general principles of the statistical properties of terms and a method to obtain the corresponding language specific parameters. This paper argues in favor of a statistical approach to terminology extraction, general to all languages but with language specific parameters. © 2011 Sociedad Española Para el Procesamiento del Lenguaje Natural. This approach has been applied to three domains (astronomy, chemistry, economics and medicine) and two languages (English and Spanish). The results show that this resource may be used for this task overcoming some of the limitations of alternative knowledge sources. The system has been evaluated substituting by it the term candidates analyzer module of an state-of-the-art term extractor, YATE. The set of titles of recovered pages and categories is selected as initial domain term vocabulary. After obtaining the full set of categories belonging to the selected domain, the collection of corresponding pages is extracted, using some constraints. The idea is to take profit of category graph of Wikipedia starting with a set of categories that we associate with the domain. The XAML file and the ViewModel are linked through XAML-based data bindings, which are described in detail in the series of series of articles on Data Binding, and particularly the The Command Interface article.In this paper we present a new approach for obtaining the terminology of a given domain using the category and page structures of the Wikipedia in a domain and language independent way. The calculator logic is encapsulated in the RpnCalculatorViewModel.cs file. The code-behind file switches between these two layouts based on the relative width and height of the page. The layout of the keys appears twice in the MainPage.xaml file, separately for portrait mode and landscape mode. The + operation adds 3 and 4, the times operation multiplies 5 and that result, and the minus operation subtracts 2 from that result. Binary operations (such as + and /) pop two numbers from the stack, perform the operation, and push the result on the stack. ![]() Unary operations (such as log and sin) pop a number from the stack, apply the operation, and push the result back on the stack. Numbers are pushed on the stack by pressing the ENTER key. RPN (also called postfix notation) is described in the Wikipedia article Reverse Polish notation and the RPN Calculator workbook, which shows an alternative approach to coding an RPN calculator for Xamarin.Forms. ![]() An RPN (Reverse Polish Notation) calculator allows numbers and operations to be entered without parentheses or an equal key.
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