Text Segmentation of Digitized Clinical Texts

Cyril Grouin


Abstract
In this paper, we present the experiments we made to recover the original page layout structure into two columns from layout damaged digitized files. We designed several CRF-based approaches, either to identify column separator or to classify each token from each line into left or right columns. We achieved our best results with a model trained on homogeneous corpora (only files composed of 2 columns) when classifying each token into left or right columns (overall F-measure of 0.968). Our experiments show it is possible to recover the original layout in columns of digitized documents with results of quality.
Anthology ID:
L16-1570
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3592–3599
Language:
URL:
https://aclanthology.org/L16-1570
DOI:
Bibkey:
Cite (ACL):
Cyril Grouin. 2016. Text Segmentation of Digitized Clinical Texts. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3592–3599, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Text Segmentation of Digitized Clinical Texts (Grouin, LREC 2016)
Copy Citation:
PDF:
https://aclanthology.org/L16-1570.pdf