@inproceedings{cohan-etal-2019-structural,
title = "Structural Scaffolds for Citation Intent Classification in Scientific Publications",
author = "Cohan, Arman and
Ammar, Waleed and
van Zuylen, Madeleine and
Cady, Field",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1361",
doi = "10.18653/v1/N19-1361",
pages = "3586--3596",
abstract = "Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3{\%} absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite.",
}
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%0 Conference Proceedings
%T Structural Scaffolds for Citation Intent Classification in Scientific Publications
%A Cohan, Arman
%A Ammar, Waleed
%A van Zuylen, Madeleine
%A Cady, Field
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F cohan-etal-2019-structural
%X Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite.
%R 10.18653/v1/N19-1361
%U https://aclanthology.org/N19-1361
%U https://doi.org/10.18653/v1/N19-1361
%P 3586-3596
Markdown (Informal)
[Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://aclanthology.org/N19-1361) (Cohan et al., NAACL 2019)
ACL
- Arman Cohan, Waleed Ammar, Madeleine van Zuylen, and Field Cady. 2019. Structural Scaffolds for Citation Intent Classification in Scientific Publications. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3586–3596, Minneapolis, Minnesota. Association for Computational Linguistics.