@inproceedings{gartner-etal-2018-nlatool,
title = "{NLAT}ool: an Application for Enhanced Deep Text Understanding",
author = {G{\"a}rtner, Markus and
Mayer, Sven and
Schwind, Valentin and
H{\"a}mmerle, Eric and
Turcan, Emine and
Rheinwald, Florin and
Murawski, Gustav and
Lischke, Lars and
Kuhn, Jonas},
editor = "Zhao, Dongyan",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-2026",
pages = "118--122",
abstract = "Today, we see an ever growing number of tools supporting text annotation. Each of these tools is optimized for specific use-cases such as named entity recognition. However, we see large growing knowledge bases such as Wikipedia or the Google Knowledge Graph. In this paper, we introduce NLATool, a web application developed using a human-centered design process. The application combines supporting text annotation and enriching the text with additional information from a number of sources directly within the application. The tool assists users to efficiently recognize named entities, annotate text, and automatically provide users additional information while solving deep text understanding tasks.",
}
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<abstract>Today, we see an ever growing number of tools supporting text annotation. Each of these tools is optimized for specific use-cases such as named entity recognition. However, we see large growing knowledge bases such as Wikipedia or the Google Knowledge Graph. In this paper, we introduce NLATool, a web application developed using a human-centered design process. The application combines supporting text annotation and enriching the text with additional information from a number of sources directly within the application. The tool assists users to efficiently recognize named entities, annotate text, and automatically provide users additional information while solving deep text understanding tasks.</abstract>
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%0 Conference Proceedings
%T NLATool: an Application for Enhanced Deep Text Understanding
%A Gärtner, Markus
%A Mayer, Sven
%A Schwind, Valentin
%A Hämmerle, Eric
%A Turcan, Emine
%A Rheinwald, Florin
%A Murawski, Gustav
%A Lischke, Lars
%A Kuhn, Jonas
%Y Zhao, Dongyan
%S Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F gartner-etal-2018-nlatool
%X Today, we see an ever growing number of tools supporting text annotation. Each of these tools is optimized for specific use-cases such as named entity recognition. However, we see large growing knowledge bases such as Wikipedia or the Google Knowledge Graph. In this paper, we introduce NLATool, a web application developed using a human-centered design process. The application combines supporting text annotation and enriching the text with additional information from a number of sources directly within the application. The tool assists users to efficiently recognize named entities, annotate text, and automatically provide users additional information while solving deep text understanding tasks.
%U https://aclanthology.org/C18-2026
%P 118-122
Markdown (Informal)
[NLATool: an Application for Enhanced Deep Text Understanding](https://aclanthology.org/C18-2026) (Gärtner et al., COLING 2018)
ACL
- Markus Gärtner, Sven Mayer, Valentin Schwind, Eric Hämmerle, Emine Turcan, Florin Rheinwald, Gustav Murawski, Lars Lischke, and Jonas Kuhn. 2018. NLATool: an Application for Enhanced Deep Text Understanding. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 118–122, Santa Fe, New Mexico. Association for Computational Linguistics.