Relaciones en Hibernate

Guía basada en ejemplos para el modelado de diferentes tipos de relaciones entre objetos en Hibernate

Caché de segundo nivel en Hibernate

Breve guía basada en ejemplos para configurar y activar la caché de segundo nivel en Hibernate

Consejos para un mejor rendimiento de Hibernate

Presentación para un mejor rendimiento de la herramienta Hibernate

El Inglés y el Español en la Lingüı́stica Computacional: Traducción Automática

Presentaciones del bloque 2 de la asignatura "el Inglés y el Español en la Lingüística computacional", centrado en la traducción automática

Stand-off Annotation of Web Content as a Legally Safer Alternative to Crawling for Distribution

Sentence-aligned web-crawled parallel text or bitext is frequently used to train statistical machine translation systems. To that end, web-crawled sentence-aligned bitext sets are sometimes made publicly available and distributed by translation technologies practitioners. Contrary to what may be commonly believed, distribution of web-crawled text is far from being free from legal implications, and may sometimes actually violate the usage restrictions. As the distribution and availability of sentence-aligned bitext is key to the development of statistical machine translation systems, this paper proposes an alternative: instead of copying and distributing copies of web content in the form of sentence-aligned bitext, one could distribute a legally safer stand-off annotation of web content, that is, files that identify where the aligned sentences are, so that end users can use this annotation to privately recrawl the bitexts. The paper describes and discusses the legal and technical aspects of this proposal, and outlines an implementation.Funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran) is acknowledged

Using Machine Translation to Provide Target-Language Edit Hints in Computer Aided Translation Based on Translation Memories

This paper explores the use of general-purpose machine translation (MT) in assisting the users of computer-aided translation (CAT) systems based on translation memory (TM) to identify the target words in the translation proposals that need to be changed (either replaced or removed) or kept unedited, a task we term as "word-keeping recommendation". MT is used as a black box to align source and target sub-segments on the fly in the translation units (TUs) suggested to the user. Source-language (SL) and target-language (TL) segments in the matching TUs are segmented into overlapping sub-segments of variable length and machine-translated into the TL and the SL, respectively. The bilingual sub-segments obtained and the matching between the SL segment in the TU and the segment to be translated are employed to build the features that are then used by a binary classifier to determine the target words to be changed and those to be kept unedited. In this approach, MT results are never presented to the translator. Two approaches are presented in this work: one using a word-keeping recommendation system which can be trained on the TM used with the CAT system, and a more basic approach which does not require any training. Experiments are conducted by simulating the translation of texts in several language pairs with corpora belonging to different domains and using three different MT systems. We compare the performance obtained to that of previous works that have used statistical word alignment for word-keeping recommendation, and show that the MT-based approaches presented in this paper are more accurate in most scenarios. In particular, our results confirm that the MT-based approaches are better than the alignment-based approach when using models trained on out-of-domain TMs. Additional experiments were performed to check how dependent the MT-based recommender is on the language pair and MT system used for training. These experiments confirm a high degree of reusability of the recommendation models across various MT systems, but a low level of reusability across language pairs.This work is supported by the Spanish government through projects TIN2009-14009-C02-01 and TIN2012-32615

Herramientas para Hibernate

Presentación sobre herramientas complementarias para Hibernate

Consultas con Hibernate

Presentació sobre la realització de consultes en Hibernate

Hibernate para Object/Relational Mapping (ORM)

Presentación introductoria para el mapeo objetos/relacional