Studying the Evolution of Scientific Topics and their Relationships

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1908–1922 August 1–6, 2021. ©2021 Association for Computational Linguistics 1908 Studying the Evolution of Scientific Topics and their Relationships Ana Sabina Uban♦,♠ Cornelia Caragea♣ Liviu P. Dinu♦ ♦Human Languages Technologies Research Center, University of Bucharest, Bucharest, Romania ♠Universitat Polit`ecnica de Val`encia, Val`encia, Spain ♣Computer Science, University of Illinois at Chicago, Chicago, Illinois ana.uban+acad@gmail.com, cornelia@uic.edu, ldinu@fmi.unibuc.ro Abstract We propose a study of the development of scientific topics through time, as well as the relations between them within the scientific field of computational linguistics and across subfields. We use topic modeling to analyze scientific texts published in the ACL Anthol- ogy, and introduce a categorization of topics in our field into 3 types: tasks, algorithms, and data. In order to understand how top- ics emerge, evolve, and gradually disappear over time, we analyze the evolution of these topics across time through several case stud- ies. We further include in our analysis papers published in NeurIPS, and try to understand whether there was any influence between top- ics in this conference focused on neural meth- ods and computational linguistics conferences, as well as measure the divergence over time between conferences in terms of the topics ap- proached. We additionally look at the relation- ships between topics, categorizing them into types of competing or cooperating topics. 1 Introduction Scientific fields progress through innovation. Sci- ence functions under the premise that, when new better topics appear in research, they overtake the old ones and contribute to shaping the progress of the research field (Kuhn, 2012). Nevertheless, scientific topics evolve interdependently (the ap- pearance and popularity of one topic may affect the popularity of another) and oftentimes, the focus of research in a certain field is also influenced by topics in other related subfields. We propose a multidimensional approach for studying scientific topics and their evolution, by analyzing our field of research - computational lin- guistics - from several points of view: we look at the parallel evolution of topics in computational linguistics and their popularity over time, as well as how they relate to each other, engaging in coop- erating or competing relationships. We also extend this perspective by considering the interplay of top- ics within a field, as well as the context in which they appear, and how the same topic is portrayed in different subfields, with a focus on the mutual in- fluence between ideas in computational linguistics and those in the related field of neural networks. Among studies that track the evolution of topics in scientific texts, Hall et al. (2008) focused on sci- entific text in computational linguistics, analyzing papers published in ACL, EMNLP and COLING between 1978 and 2006. The authors identify in- creasing and decreasing trends up to 2006, and make predictions about the subsequent evolution of the field. We continue the analysis including articles published up until the end of 2018, and uncover current shifts and trends that may not have been predictable 15 years ago - such …

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