@inproceedings{du-etal-2023-autodive,
title = "Autodive: An Integrated Onsite Scientific Literature Annotation Tool",
author = "Du, Yi and
Wang, Ludi and
Huang, Mengyi and
Song, Dongze and
Cui, Wenjuan and
Zhou, Yuanchun",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.7",
doi = "10.18653/v1/2023.acl-demo.7",
pages = "76--85",
abstract = "Scientific literature is always available in Adobe{'}s Portable Document Format (PDF), which is friendly for scientists to read. Compared with raw text, annotating directly on PDF documents can greatly improve the labeling efficiency of scientists whose annotation costs are very high. In this paper, we present Autodive, an integrated onsite scientific literature annotation tool for natural scientists and Natural Language Processing (NLP) researchers. This tool provides six core functions of annotation that support the whole lifecycle of corpus generation including i)annotation project management, ii)resource management, iii)ontology management, iv)manual annotation, v)onsite auto annotation, and vi)annotation task statistic. Two experiments are carried out to verify efficiency of the presented tool. A live demo of Autodive is available at \url{http://autodive.sciwiki.cn}. The source code is available at \url{https://github.com/Autodive}.",
}
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<abstract>Scientific literature is always available in Adobe’s Portable Document Format (PDF), which is friendly for scientists to read. Compared with raw text, annotating directly on PDF documents can greatly improve the labeling efficiency of scientists whose annotation costs are very high. In this paper, we present Autodive, an integrated onsite scientific literature annotation tool for natural scientists and Natural Language Processing (NLP) researchers. This tool provides six core functions of annotation that support the whole lifecycle of corpus generation including i)annotation project management, ii)resource management, iii)ontology management, iv)manual annotation, v)onsite auto annotation, and vi)annotation task statistic. Two experiments are carried out to verify efficiency of the presented tool. A live demo of Autodive is available at http://autodive.sciwiki.cn. The source code is available at https://github.com/Autodive.</abstract>
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%0 Conference Proceedings
%T Autodive: An Integrated Onsite Scientific Literature Annotation Tool
%A Du, Yi
%A Wang, Ludi
%A Huang, Mengyi
%A Song, Dongze
%A Cui, Wenjuan
%A Zhou, Yuanchun
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F du-etal-2023-autodive
%X Scientific literature is always available in Adobe’s Portable Document Format (PDF), which is friendly for scientists to read. Compared with raw text, annotating directly on PDF documents can greatly improve the labeling efficiency of scientists whose annotation costs are very high. In this paper, we present Autodive, an integrated onsite scientific literature annotation tool for natural scientists and Natural Language Processing (NLP) researchers. This tool provides six core functions of annotation that support the whole lifecycle of corpus generation including i)annotation project management, ii)resource management, iii)ontology management, iv)manual annotation, v)onsite auto annotation, and vi)annotation task statistic. Two experiments are carried out to verify efficiency of the presented tool. A live demo of Autodive is available at http://autodive.sciwiki.cn. The source code is available at https://github.com/Autodive.
%R 10.18653/v1/2023.acl-demo.7
%U https://aclanthology.org/2023.acl-demo.7
%U https://doi.org/10.18653/v1/2023.acl-demo.7
%P 76-85
Markdown (Informal)
[Autodive: An Integrated Onsite Scientific Literature Annotation Tool](https://aclanthology.org/2023.acl-demo.7) (Du et al., ACL 2023)
ACL
- Yi Du, Ludi Wang, Mengyi Huang, Dongze Song, Wenjuan Cui, and Yuanchun Zhou. 2023. Autodive: An Integrated Onsite Scientific Literature Annotation Tool. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 76–85, Toronto, Canada. Association for Computational Linguistics.