Citation Intent Classification

SciCite is a dataset of citation intents that addresses multiple scientific domains and is more than five times larger than ACL-ARC. Source: Structural Scaffolds for Citation Intent Classification in Scientific Publications Image Source: https://arxiv.org/pdf/1904.01608v2.pdf

ACL Anthology Reference Corpus (ACL ARC) is a collection of 10,920 academic papers from the ACL Anthology. ACL ARC is cleaned to remove: - files that look like not full papers, paper fragments, foreign-language papers (e.g., French), or pure junk. - headers (title and author information; NOT abstract). - footers ("References" line and the actual references). - some bad characters (spurious characters). - some page numbers (i.e., a single number appearing on a line, with nothing else attached to it). - significant foreign-language (e.g., French) content in an otherwise English paper. The cleaned corpus has 10,628 documents. Source: ACL ARC

A scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata. The unarXive data set contains * One million papers in plain text * 63 million citation contexts * 39 million reference strings * A citation network of 16 million connections The data is generated from all LaTeX sources on arXiv from 1991–2020/07 and therefore of higher quality than data generated from PDF files. Furthermore, as all citing papers are available in full text, citation contexts of arbitrary size can be extracted. Typical uses of the data set are approaches in * Citation recommendation * Citation context analysis * Reference string parsing The code for generating the data set is publicly available.

Internet Archive Scholar Reference Dataset.