Abstract

Citations are scientists’ tools for grounding their innovations and findings in the existing collective knowledge. They are used for semantically distinct purposes as scientists utilize them at different parts of their work to convey specific information. As a result, a crucial aspect of scientific document understanding is recognizing the authorial intent associated with citations. Current state-of-the-art methods rely on contextual sentences surrounding each citation to classify the intent. However, in the absence of textual content, these approaches become unusable. In this work, we propose a text-free citation intent classification method built on relational information among scholarly works in this work. To this end, we introduce a large-scale knowledge graph built from the publications in the SciCite dataset and their multi-hop neighborhood extracted from The Semantic Scholar Open Research Corpus (S2ORC). We also augment this knowledge graph by adding weakly-labeled links based on the intent information available in the S2ORC. Finally, we cast the intent classification task as a link prediction problem on the newly created knowledge graph. We study this problem in both transductive and inductive settings. Our experimental results show that we can achieve a comparable macro F1 score to word embedding content-based methods by only relying on features and relations derived from this knowledge graph. Specifically, we achieve macro F1 scores of 62.16 and 59.81 in the transductive and inductive settings, respectively, on the link-level SciCite dataset. Moreover, by combining our method with the state-of-the-art NLP-based model, we achieve …

Metadata

publication

SDU Workshop @ AAAI 2023, 2023

year

2023

publication date

2023/2/14

authors

Xinwei Du, Kian Ahrabian, Arun Baalaaji Sankar Ananthan, Richard Delwin Myloth, Jay Pujara

link

https://ceur-ws.org/Vol-3656/paper8.pdf

resource_link

https://ceur-ws.org/Vol-3656/paper8.pdf

conference

SDU Workshop @ AAAI 2023