Cifar: tratamiento textual

Tratamiento de la implicación textual a través de patrones semánticos

Resumen. En el presente trabajo se presenta una nueva metodología para el Problema de Implicación Textual. Esta metodología propone el empate de patrones sintácticos, los cuales están construidos por la cantidad de términos. Esta aproximación es una variante del modelo de aproximación por empatamiento. Esta metodología fue probada y validada con tres corpus ofrecidos por varias conferencias int...

Convolutional Deep Belief Networks on CIFAR-10

We describe how to train a two-layer convolutional Deep Belief Network (DBN) on the 1.6 million tiny images dataset. When training a convolutional DBN, one must decide what to do with the edge pixels of teh images. As the pixels near the edge of an image contribute to the fewest convolutional lter outputs, the model may see it t to tailor its few convolutional lters to better model the edge pix...

CIFAR Neural Computation and Adaptive Perception (NCAP) Program Meeting

Tratamiento de la mucositis en

Objective: To identify therapeutic measures to reduce the severity of oral mucositis in adult patients undergoing bone marrow transplantation (BMT). Methods: A systematic review using the following databases: LILACS, MEDLINE, CINAHL, EMBASE, CENTRAL (Cochrane Central) and DARE (Database of abstracts of reviews of effects), for the period between 1972 to July 2010, using the key words mucositis,...

Textual Entailment

The task of Recognizing Textual Entailment (RTE) requires automated systems to identify when two spans of text share a common meaning -for example, that ``Alphaville Inc.'s attempted acquisition of Bauhaus led to a jump in both companies' stock prices'' entails ``Bauahaus' stock rose'', but not ``Alphaville acquired Bauhaus''. This general capability would be a solid proxy for Natural Language ...