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Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java. At its core, it provides much the same functionality as the core of Moses. Distinctive features include: providing an easy to use API for implementing new decoding model features, the ability to translating using phrases that include gaps (Galley et al. 2010), and conditional extraction of phrase-tables and lexical reordering models. Developed by The Natural Language Processing Group...

This project is contains implementations of algorithms to integrate the output of different NLP tools (part of speech taggers, morphologies, parsers, etc.) in order to obtain more accurate, more robust and more fine-grained linguistic analyses. Note that the code is outdated, but left here for documentation purposes. Its functionality may be reimplemented within the NLP2RDF project (http://code.google.com/p/nlp2rdf).

PRDL is one of the core components within the ENDORSE project. The scope of the language is to encompass clauses from data protection legislation and enterprise privacy policies in order to e.g. derive data access decisions automatically based on the enterprise privacy policies (EPPs). There have been many initiatives for expressing privacy rules and legal restrictions into a computable way. The attempt of PRDL is to present a collaborative result towards a multistakeholder language. The goal was that PRDL should be sufficiently expressive to define EPPs for SMEs, it should link the wording of the data privacy laws of different European countries, and it should be represented in natural language and therefore should be easy to understand. ...