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 e-book constitutes the refereed complaints of the fifth foreign Workshop on managed ordinary Language, CNL 2016, held in Aberdeen, united kingdom, in July 2016. The eleven complete papers provided have been conscientiously reviewed and chosen from 15 submissions. the subjects diversity from normal languages that are managed, to managed languages with a usual language flavour; and from extra theoretical effects to interfaces, reasoning engines and real-life functions of CNLs.

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

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Afr. Stud. 17(2), 66–88 (2008) 5. : Natural language generation in the context of the semantic web. Semant. Web J. 5(6), 493–513 (2014) 6. : Is lemon sufficient for building multilingual ontologies for Bantu languages? In: Proceedings of OWLED 2014, CEUR-WS, vol. 1265, pp. 61–72, Riva del Garda, Italy (2014) 7. : Bootstrapping in language resource generation. In: Proceedings of PRASA 2003, Langebaan, South Africa (2003) 8. : Towards a more natural multilingual controlled language interface to owl.

40(1), 121–170 (2014) 16. : Nugl online: The online version of the updated guthrie list, a referential classification of the bantu languages (2009). pdf 17. : CNLs for the semantic web: a state of the art. Lang. Resour. Eval. 1–30 (2016, in print). 1007/s10579-016-9351-x 18. : Demonstrative determiners in Runyankore-Rukiga. Master’s thesis, Norwegian University of Science and Technology, Norway (2014) 19. : A Simplified Runyankore-Rukiga-English Dictionary. Fountain Publishers, Kampala (2009) 20.

Next we discuss results for model perplexity obtained from texts corrected automatically using word prediction. The methodology was similar to the approach followed above, but instead of analysing rank, the target word is substituted by the top suggestion and printed to an output file. When all predictions are generated, the distance between the perplexities of the original and predicted texts is calculated. The intuition is that as the original test 44 S. Palmaz et al. Fig. 3. Effects of adding character context to word context prediction data is a correct controlled language target and an ideal word prediction system would be one which produces the same text from the top results with a resulting perplexity distance of zero.

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