Tobias Kuhn


2021

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Proceedings of the Seventh International Workshop on Controlled Natural Language (CNL 2020/21)
Tobias Kuhn | Silvie Spreeuwenberg | Stijn Hoppenbrouwers | Norbert E. Fuchs
Proceedings of the Seventh International Workshop on Controlled Natural Language (CNL 2020/21)

2020

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Provenance for Linguistic Corpora through Nanopublications
Timo Lek | Anna de Groot | Tobias Kuhn | Roser Morante
Proceedings of the 14th Linguistic Annotation Workshop

Research in Computational Linguistics is dependent on text corpora for training and testing new tools and methodologies. While there exists a plethora of annotated linguistic information, these corpora are often not interoperable without significant manual work. Moreover, these annota-tions might have evolved into different versions, making it challenging for researchers to know the data’s provenance. This paper addresses this issue with a case study on event annotated corpora and by creating a new, more interoperable representation of this data in the form of nanopublications. We demonstrate how linguistic annotations from separate corpora can be reliably linked from the start, and thereby be accessed and queried as if they were a single dataset. We describe how such nanopublications can be created and demonstrate how SPARQL queries can be performed to extract interesting content from the new representations. The queries show that information of multiple corpora can be retrieved more easily and effectively because the information of different corpora is represented in a uniform data format.

2014

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A Survey and Classification of Controlled Natural Languages
Tobias Kuhn
Computational Linguistics, Volume 40, Issue 1 - March 2014

2008

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Writing Support for Controlled Natural Languages
Tobias Kuhn | Rolf Schwitter
Proceedings of the Australasian Language Technology Association Workshop 2008

2007

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Pro3Gres Parser in the CoNLL Domain Adaptation Shared Task
Gerold Schneider | Kaarel Kaljurand | Fabio Rinaldi | Tobias Kuhn
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)