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Background We published the Canadian 2003 International Consensus Algorithm for the Diagnosis, Therapy, and Management of Hereditary Angioedema (HAE; C1 inhibitor [C1-INH] deficiency) and updated this as Hereditary angioedema: a current state-of-the-art review: Canadian Hungarian 2007 International Consensus Algorithm for the Diagnosis, Therapy, and Management of Hereditary Angioedema. Objective To update the International Consensus Algorithm for the Diagnosis, Therapy and Management of Hereditary Angioedema (circa 2010). Methods The Canadian Hereditary Angioedema Network (CHAEN)/Reseau Canadien d'angioedeme hereditaire (RCAH) (www.haecanada.com) and cosponsors University of Calgary and the Canadian Society of Allergy and Clinical Immunology (with an unrestricted educational grant from CSL Behring) held our third Conference May 15th to 16th, 2010 in Toronto Canada to update our consensus approach. The Consensus document was reviewed at the meeting and then circulated for review. Results This manuscript is the 2010 International Consensus Algorithm for the Diagnosis, Therapy and Management of Hereditary Angioedema that resulted from that conference. Conclusions Consensus approach is only an interim guide to a complex disorder such as HAE and should be replaced as soon as possible with large phase III and IV clinical trials, meta analyses, and using data base registry validation of approaches including quality of life and cost benefit analyses, followed by large head-to-head clinical trials and then evidence-based guidelines and standards for HAE disease management.
We present a method for automatic RMRS semantics construction from dependency structures, following the semantic algebra of Copestake et al. (2001). We have applied this method to a subset of the TIGER Dependency Bank for German (Forst et al., 2004) to obtain a semantic treebank for (HPSG) parser evaluation. We describe the semantics construction mechanism and give evaluation figures from manual validation of the treebank. These indicate high precision of the automatic RMRS construction process.
We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis.
We present a constraint-based syntax-semantics interface for the construction of RMRS (Robust Minimal Recursion Semantics) representations from shallow grammars. The architecture is designed to allow modular interfaces to existing shallow grammars of various depth - ranging from chunk grammars to context-free stochastic grammars. We define modular semantics construction principles in a typed feature structure formalism that allow flexible adaptation to alternative grammars and different languages.