SciWING– A Software Toolkit for Scientific Document Processing

Abhinav Ramesh Kashyap, Min-Yen Kan


Abstract
We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from http://sciwing.io and the demos are available at http://rebrand.ly/sciwing-demo.
Anthology ID:
2020.sdp-1.13
Volume:
Proceedings of the First Workshop on Scholarly Document Processing
Month:
November
Year:
2020
Address:
Online
Editors:
Muthu Kumar Chandrasekaran, Anita de Waard, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Petr Knoth, David Konopnicki, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer
Venue:
sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
113–120
Language:
URL:
https://aclanthology.org/2020.sdp-1.13
DOI:
10.18653/v1/2020.sdp-1.13
Bibkey:
Cite (ACL):
Abhinav Ramesh Kashyap and Min-Yen Kan. 2020. SciWING– A Software Toolkit for Scientific Document Processing. In Proceedings of the First Workshop on Scholarly Document Processing, pages 113–120, Online. Association for Computational Linguistics.
Cite (Informal):
SciWING– A Software Toolkit for Scientific Document Processing (Ramesh Kashyap & Kan, sdp 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.sdp-1.13.pdf
Video:
 https://slideslive.com/38940731
Data
Semantic Scholar