An Analysis of Acknowledgments in NLP Conference Proceedings (2022)

Abstract: While acknowledgments are often overlooked and sometimes entirely missing from publications, this short section of a paper can provide insights on the state of a field. We characterize and perform a textual analysis of acknowledgments in NLP conference proceedings across the last 17 years, revealing broader trends in funding and research directions in NLP as well as interesting phenomena including career incentives and the influence of defaults.

## introduction a research project is seldom a solo endeavor. different entities contribute ideas, expertise, labor, money, and many other factors that lead to a successful project. in a publication, the most salient contributors are the authors, whose names are front and center on page one. in this paper, we investigate the so-called "lesser" contributors, whose names exist in the acknowledgments section of a publication, typically right before the references. specifically, we ask several research questions: our analysis of acknowledgments in acl and emnlp conference proceedings presents a view of the state of the field of natural language processing, including: acknowledgments have been investigated in both the social sciences and computer science communities. @xcite analyze acknowledgments in history students' dissertations. @xcite performed a cursory analysis of funding acknowledgments in thomson reuter's web of science database. @xcite analyzed computer science articles from the citeseer database foot_0 , identifying the most common acknowledged entities. part of our work is similar in design but focuses specifically on the field of nlp rather than the broader field of computer science. @xcite performed a qualitative analysis of acknowledgments, looking at word usage patterns. our study goes into more depth, looking at specific entities that are acknowledged, and what they are acknowledged for. grant funding is typically acknowledged in the acknowledgments section, and there is some recent interest in identifying funding sources and grant numbers as an information extraction task @xcite . our paper does not tackle the task of grant funding detection but rather analyzes general trends in grant funding, as well as other trends. within the nlp community, a line of work has extracted insights from trends and citations in nlp publications (mohammad, 2020a,b,c,d), but has not focused specifically on acknowledgments. ## data we analyze proceedings of two conferences: the annual meeting of the association for computational linguistics (acl), and the conference on empirical methods in natural language processing (emnlp). acl and emnlp are toptier international nlp conferences with a broad scope and thus would be representative of the broader nlp community. specifically, we examine long and short papers published in the main conference proceedings from 2005-2021. 2 we download the proceeding pdfs from the acl anthology, foot_2 splitting the file into separate papers and extracting text using pymupdf. 4 we extract the acknowledgments section by searching for the word acknowledgments and its spelling variants, followed by some manual cleaning efforts. we then perform dependency parsing and named entity recognition on all acknowledgments using spacy's en_core_news_lg model. 5 figure 1 presents the total number of acl and emnlp papers per year, from which we extract a total of 7,838 acknowledgments. ## characterizing acknowledgments this section, which forms the bulk of our paper, investigates several research questions that can be answered by analyzing papers' acknowledgments. ## conclusion while acknowledgments are seemingly insignificant and often entirely missing, in this paper we show that much can be gleaned from this short section in publications. our analysis of acknowledgments in nlp conference proceedings reveal larger trends about the state of nlp research. grant funding from government agencies and industry companies show increases in international participation and funding, especially from chinese funding agencies. grant acknowledgments also hint at the life-cycle and productivity of the grants. we identify the year 2014 as an important year of research using neural networks, corresponding with a dramatic increase in chinese funding and industry gpu gifts. textual analyses also reveal what researchers are most thankful for, and that some researchers indicate corresponding author, a career incentive specific to chinese researchers. finally, an analysis of spelling variation reveals the influence of defaults on the authors' choice of section headers. as the field of nlp continues to grow, followup analyses will help bring to light more insights about the field and its behind-thescenes contributors, without whom all these papers would not have been published.