In this paper, we show an approach to cross-lingual textual entailment (CLTE) by using machine translation systems such as Bing Translator and Google Translate. We experiment with a wide variety of data sets to the task of textual Entailment (TE) and evaluate the contribution of an algorithm that expands a monolingual TE corpus that seems promising for the task of CLTE. We built a CLTE corpus and we report a procedure that can be used to create a CLTE corpus in any pair of languages. We also report the results obtained in our experiments with the three-way classification task for CLTE and we show that this result outperform the average score of RTE (Recognizing Textual Entailment) systems. Finally, we find that using WordNet as the only source of lexical-semantic knowledge it is possibly to build a system for CLTE, which achieves comparable results with the average score of RTE systems for both two-way and three-way tasks.
Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.
En el presente trabajo se plantea el desarrollo de un modelo para detección de similitudes de código fuente para poder determinar la existencia de prácticas de reutilización aplicando técnicas vinculadas al aprendizaje automático con un enfoque a la lingüística computacional. Existen diversas técnicas desarrolladas por diversos autores que permiten la detección de fragmentos de código fuente similares (usualmente llamados Clones de Código o Code Clones) enfocados en los distintos tipos de clones. La identificación de estos clones de código fuente puede servir para varios propósitos, entre los que se puede mencionar el estudio de la evolución del código fuente de un proyecto, detección de prácticas de reutilización, extracción de un fragmento de código para “refactorización” del mismo, detección y seguimiento de defectos, fallas y/o virus para su corrección, entre otros.
En la presente propuesta de tesis se plantea el desarrollo de un modelo para detección de similitudes de código fuente para poder determinar la existencia de prácticas de reutilización aplicando técnicas vinculadas a la lingüística computacional, tales como minería de datos sobre texto y procesamiento del lenguaje natural. La identificación de similitudes de código puede servir para varios propósitos, entre los que se puede mencionar el estudio de la evolución del código fuente de un proyecto, detección de prácticas de reutilización, extracción de un fragmento de código para “refactorización” del mismo, seguimiento de defectos para su corrección, entre otros.
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