Exploring the Landscape of Natural Language Processing Research

Proceedings of Recent Advances in Natural Language Processing, pages 1034–1045 Varna, Sep 4–6, 2023 https://doi.org/10.26615/978-954-452-092-2_111 1034 Exploring the Landscape of Natural Language Processing Research Tim Schopf, Karim Arabi, and Florian Matthes Technical University of Munich, Department of Computer Science, Germany {tim.schopf,karim.arabi,matthes}@tum.de Abstract As an efficient approach to understand, gen- erate, and process natural language texts, re- search in natural language processing (NLP) has exhibited a rapid spread and wide adop- tion in recent years. Given the increasing re- search work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. Contributing to closing this gap, we have systematically classified and an- alyzed research papers in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent devel- opments in NLP, summarize our findings, and highlight directions for future work. 1 1 Introduction Natural language is a fundamental aspect of hu- man communication and inherent to human utter- ances and information sharing. Accordingly, most human-generated digital data are composed in nat- ural language. Given the ever-increasing amount and importance of digital data, it is not surprising that computational linguists have started develop- ing ideas on enabling machines to understand, gen- erate, and process natural language since the 1950s (Hutchins, 1999). More recently, the introduction of the trans- former model (Vaswani et al., 2017) and pretrained language models (Radford and Narasimhan, 2018; Devlin et al., 2019) have sparked increasing inter- est in natural language processing (NLP). Submis- sions on various NLP topics and applications are being published in a growing number of journals and conferences, such as TACL, ACL, and EMNLP, 1Code available: https://github.com/sebischair/Exploring- NLP-Research as well as in several smaller workshops that focus on specific areas. Thereby, the ACL Anthology2 as a repository for publications from many major NLP journals, conferences, and workshops emerges as an important tool for researchers. As of January 2023, it provides access to over 80,000 articles published since 1952. Figure 1 shows the distribu- tion of publications in the ACL Anthology over the 50-year observation period. 1960 1970 1980 1990 2000 2010 2020 Year 0 1000 2000 3000 4000 5000 6000 7000 8000 Number of Papers Figure 1: Distribution of the number of papers per year in the ACL Anthology from 1952 to 2022. Accompanying the increase in publications, there has also been a growth in the number of differ- ent fields of study (FoS) that have been researched within the NLP domain. FoS are academic dis- ciplines and concepts that usually consist of (but are not limited to) tasks or techniques (Shen et al., 2018). Given the rapid developments in NLP re- search, obtaining an overview of the domain and maintaining it is difficult. As such, collecting in- sights, consolidating existing results, and present- ing a structured overview of the field is important. However, to the best of our …

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