@inproceedings{748dc30b9245489b864a27b89630fe3c,
title = "Citation Intent Classification and Its Supporting Evidence Extraction for Citation Graph Construction",
abstract = "As the significant growth of scientific publications in recent years, an efficient way to extract scholarly knowledge and organize the relationship among literature is necessitated. Previous works constructed scientific knowledge graph with authors, papers, citations, and scientific entities. To assist researchers to grasp the research context comprehensively, this paper constructs a fine-grained citation graph in which citation intents and their supporting evidence are labeled between citing and cited papers instead. We propose a model with a Transformer encoder to encode the long-lengthy paper. To capture the coreference relations of words and sentences in a paper, a coreference graph is created by utilizing Gated Graph Convolution Network (GGCN). We further propose a graph modification mechanism to dynamically update the coreference links. Experimental results show that our model achieves promising results on identifying multiple citation intents in sentences.",
keywords = "Citation Graph Construction, Citation Intent, Intent Evidence",
author = "Tsai, {Hong Jin} and Yen, {An Zi} and Huang, {Hen Hsen} and Chen, {Hsin Hsi}",
note = "Publisher Copyright: {\textcopyright} 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 ; Conference date: 21-10-2023 Through 25-10-2023",
year = "2023",
month = oct,
day = "21",
doi = "10.1145/3583780.3614808",
language = "English",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "2472--2481",
booktitle = "CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management",
}