Examining Citations of Natural Language Processing Literature

Saif M. Mohammad


Abstract
We extracted information from the ACL Anthology (AA) and Google Scholar (GS) to examine trends in citations of NLP papers. We explore questions such as: how well cited are papers of different types (journal articles, conference papers, demo papers, etc.)? how well cited are papers from different areas of within NLP? etc. Notably, we show that only about 56% of the papers in AA are cited ten or more times. CL Journal has the most cited papers, but its citation dominance has lessened in recent years. On average, long papers get almost three times as many citations as short papers; and papers on sentiment classification, anaphora resolution, and entity recognition have the highest median citations. The analyses presented here, and the associated dataset of NLP papers mapped to citations, have a number of uses including: understanding how the field is growing and quantifying the impact of different types of papers.
Anthology ID:
2020.acl-main.464
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5199–5209
Language:
URL:
https://aclanthology.org/2020.acl-main.464
DOI:
10.18653/v1/2020.acl-main.464
Bibkey:
Cite (ACL):
Saif M. Mohammad. 2020. Examining Citations of Natural Language Processing Literature. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5199–5209, Online. Association for Computational Linguistics.
Cite (Informal):
Examining Citations of Natural Language Processing Literature (Mohammad, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.464.pdf
Video:
 http://slideslive.com/38929236