@inproceedings{nambanoor-kunnath-etal-2022-dynamic,
title = "Dynamic Context Extraction for Citation Classification",
author = "Nambanoor Kunnath, Suchetha and
Pride, David and
Knoth, Petr",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "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 = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.41",
doi = "10.18653/v1/2022.aacl-main.41",
pages = "539--549",
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: \url{https://github.com/oacore/dynamic_citation_context}",
}
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<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</abstract>
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%0 Conference Proceedings
%T Dynamic Context Extraction for Citation Classification
%A Nambanoor Kunnath, Suchetha
%A Pride, David
%A Knoth, Petr
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S 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)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F nambanoor-kunnath-etal-2022-dynamic
%X 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
%R 10.18653/v1/2022.aacl-main.41
%U https://aclanthology.org/2022.aacl-main.41
%U https://doi.org/10.18653/v1/2022.aacl-main.41
%P 539-549
Markdown (Informal)
[Dynamic Context Extraction for Citation Classification](https://aclanthology.org/2022.aacl-main.41) (Nambanoor Kunnath et al., AACL-IJCNLP 2022)
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.