Abstract. This collaborative report highlights the properties and prospects of Controlled Natural Languages (CNLs). The report poses a range of questions concerning the goals of the CNL, the design, the linguistic aspects, the relationships and evaluation of CNLs, and the application tools. In posing the questions, the report attempts to structure the field of CNLs and to encourage further systematic discussion by researchers and developers.
We present an algorithm for incremental statistical parsing with Parallel Multiple Context-Free Grammars (PMCFG). This is an extension of the algorithm by Angelov (2009) to which we added statistical ranking. We show that the new algorithm is several times faster than other statistical PMCFG parsing algorithms on real-sized grammars. At the same time the algorithm is more general since it supports non-binarized and non-linear grammars.We also show that if we make the search heuristics non-admissible, the parsing speed improves even further, at the risk of returning sub-optimal solutions.
Abstract. The paper introduces GF, Grammatical Framework, as a tool for implementing controlled languages. GF provides a high-level grammar formalism and a resource grammar library that make it easy to write grammars that cover similar fragments in several natural languages at the same time. Authoring help tools and automatic translation are provided for all grammars. As an example, a grammar of Attempto Controlled English is implemented and then ported to French, German, and Swedish.
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