Download Controlled Natural Language: 5th International Workshop, CNL by Brian Davis, Gordon J. Pace, Adam Wyner PDF

By Brian Davis, Gordon J. Pace, Adam Wyner

This publication constitutes the refereed lawsuits of the fifth overseas Workshop on managed traditional Language, CNL 2016, held in Aberdeen, united kingdom, in July 2016. The eleven complete papers offered have been rigorously reviewed and chosen from 15 submissions. the subjects diversity from ordinary languages that are managed, to managed languages with a typical language flavour; and from extra theoretical effects to interfaces, reasoning engines and real-life purposes of CNLs.

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Additional resources for Controlled Natural Language: 5th International Workshop, CNL 2016, Aberdeen, UK, July 25-27, 2016, Proceedings

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4 % was the one which had hakiri before the verb and included -mwe. The evaluation of ¬∃eats also produced interesting results. 3 %). 8 %). Despite this, the evaluation still showed that the plural is preferred for negation of roles. 34 6 J. Byamugisha et al. Discussion Kuhn [15] stated that one of the applications of CNLs is to provide natural and intuitive representations of formal languages. Verbalization is one of the ways of doing this, and our work here further solidifies what was done in [13,14] to show that it is possible for Bantu languages, despite their complex linguistic structure.

Acknowledgements. This work is based on the research supported by the Hasso Plattner Institute (HPI) Research School in CS4A at UCT and the National Research Foundation of South Africa (Grant Number 93397). References 1. : Implementing controlled languages in GF. E. ) CNL 2009. LNCS, vol. 5972, pp. 82–101. Springer, Heidelberg (2010) 2. : Definiteness and specificity in Runyankore-Rukiga. D. thesis, Stallenbosch University, Cape Town, South Africa (2014) 3. F. ): The Description Logics Handbook - Theory and Applications, 2nd edn.

From the resulting domain corpus 2850 sentences (1 %) were extracted randomly for testing and the remaining data was used for training. This amount of test sentences was chosen to keep as much data as possible for training models while keeping a usable test size. Additionally, sentences shorter than the maximum n-gram size were discarded, with a final training size of 238332 sentences. All language models were estimated with the KenLM library [19] using modified Kneser-Ney smoothing without pruning.

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