ROOTS: a toolkit for easy, fast and consistent processing of large sequential annotated data collections

Jonathan Chevelu, Gwénolé Lecorvé, Damien Lolive


Abstract
The development of new methods for given speech and natural language processing tasks usually consists in annotating large corpora of data before applying machine learning techniques to train models or to extract information. Beyond scientific aspects, creating and managing such annotated data sets is a recurrent problem. While using human annotators is obviously expensive in time and money, relying on automatic annotation processes is not a simple solution neither. Typically, the high diversity of annotation tools and of data formats, as well as the lack of efficient middleware to interface them all together, make such processes very complex and painful to design. To circumvent this problem, this paper presents the toolkit ROOTS, a freshly released open source toolkit (http://roots-toolkit.gforge.inria.fr) for easy, fast and consistent management of heterogeneously annotated data. ROOTS is designed to efficiently handle massive complex sequential data and to allow quick and light prototyping, as this is often required for research purposes. To illustrate these properties, three sample applications are presented in the field of speech and language processing, though ROOTS can more generally be easily extended to other application domains.
Anthology ID:
L14-1298
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
619–626
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/338_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Jonathan Chevelu, Gwénolé Lecorvé, and Damien Lolive. 2014. ROOTS: a toolkit for easy, fast and consistent processing of large sequential annotated data collections. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 619–626, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
ROOTS: a toolkit for easy, fast and consistent processing of large sequential annotated data collections (Chevelu et al., LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/338_Paper.pdf