Dynamic Context Extraction for Citation Classification

Suchetha Nambanoor Kunnath, David Pride, Petr Knoth


Abstract
We investigate the effect of varying citation context window sizes on model performance in citation intent classification. Prior studies have been limited to the application of fixed-size contiguous citation contexts or the use of manually curated citation contexts. We introduce a new automated unsupervised approach for the selection of a dynamic-size and potentially non-contiguous citation context, which utilises the transformer-based document representations and embedding similarities. Our experiments show that the addition of non-contiguous citing sentences improves performance beyond previous results. Evalu- ating on the (1) domain-specific (ACL-ARC) and (2) the multi-disciplinary (SDP-ACT) dataset demonstrates that the inclusion of additional context beyond the citing sentence significantly improves the citation classifi- cation model’s performance, irrespective of the dataset’s domain. We release the datasets and the source code used for the experiments at: https://github.com/oacore/dynamic_citation_context
Anthology ID:
2022.aacl-main.41
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
539–549
Language:
URL:
https://aclanthology.org/2022.aacl-main.41
DOI:
Bibkey:
Cite (ACL):
Suchetha Nambanoor Kunnath, David Pride, and Petr Knoth. 2022. Dynamic Context Extraction for Citation Classification. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 539–549, Online only. Association for Computational Linguistics.
Cite (Informal):
Dynamic Context Extraction for Citation Classification (Nambanoor Kunnath et al., AACL-IJCNLP 2022)
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PDF:
https://aclanthology.org/2022.aacl-main.41.pdf