@inproceedings{muller-etal-2021-word,
title = "Word-Level Alignment of Paper Documents with their Electronic Full-Text Counterparts",
author = {M{\"u}ller, Mark-Christoph and
Ghosh, Sucheta and
Wittig, Ulrike and
Rey, Maja},
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bionlp-1.19",
doi = "10.18653/v1/2021.bionlp-1.19",
pages = "168--179",
abstract = "We describe a simple procedure for the automatic creation of word-level alignments between printed documents and their respective full-text versions. The procedure is unsupervised, uses standard, off-the-shelf components only, and reaches an F-score of 85.01 in the basic setup and up to 86.63 when using pre- and post-processing. Potential areas of application are manual database curation (incl. document triage) and biomedical expression OCR.",
}
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<abstract>We describe a simple procedure for the automatic creation of word-level alignments between printed documents and their respective full-text versions. The procedure is unsupervised, uses standard, off-the-shelf components only, and reaches an F-score of 85.01 in the basic setup and up to 86.63 when using pre- and post-processing. Potential areas of application are manual database curation (incl. document triage) and biomedical expression OCR.</abstract>
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%0 Conference Proceedings
%T Word-Level Alignment of Paper Documents with their Electronic Full-Text Counterparts
%A Müller, Mark-Christoph
%A Ghosh, Sucheta
%A Wittig, Ulrike
%A Rey, Maja
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 20th Workshop on Biomedical Language Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F muller-etal-2021-word
%X We describe a simple procedure for the automatic creation of word-level alignments between printed documents and their respective full-text versions. The procedure is unsupervised, uses standard, off-the-shelf components only, and reaches an F-score of 85.01 in the basic setup and up to 86.63 when using pre- and post-processing. Potential areas of application are manual database curation (incl. document triage) and biomedical expression OCR.
%R 10.18653/v1/2021.bionlp-1.19
%U https://aclanthology.org/2021.bionlp-1.19
%U https://doi.org/10.18653/v1/2021.bionlp-1.19
%P 168-179
Markdown (Informal)
[Word-Level Alignment of Paper Documents with their Electronic Full-Text Counterparts](https://aclanthology.org/2021.bionlp-1.19) (Müller et al., BioNLP 2021)
ACL