Studying the History of Ideas Using Topic Models

Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 363–371, Honolulu, October 2008. c⃝2008 Association for Computational Linguistics Studying the History of Ideas Using Topic Models David Hall Symbolic Systems Stanford University Stanford, CA 94305, USA dlwh@stanford.edu Daniel Jurafsky Linguistics Stanford University Stanford, CA 94305, USA jurafsky@stanford.edu Christopher D. Manning Computer Science Stanford University Stanford, CA 94305, USA manning@stanford.edu Abstract How can the development of ideas in a sci- entific field be studied over time? We ap- ply unsupervised topic modeling to the ACL Anthology to analyze historical trends in the field of Computational Linguistics from 1978 to 2006. We induce topic clusters using Latent Dirichlet Allocation, and examine the strength of each topic over time. Our methods find trends in the field including the rise of prob- abilistic methods starting in 1988, a steady in- crease in applications, and a sharp decline of research in semantics and understanding be- tween 1978 and 2001, possibly rising again after 2001. We also introduce a model of the diversity of ideas, topic entropy, using it to show that COLING is a more diverse confer- ence than ACL, but that both conferences as well as EMNLP are becoming broader over time. Finally, we apply Jensen-Shannon di- vergence of topic distributions to show that all three conferences are converging in the topics they cover. 1 Introduction How can we identify and study the exploration of ideas in a scientific field over time, noting periods of gradual development, major ruptures, and the wax- ing and waning of both topic areas and connections with applied topics and nearby fields? One im- portant method is to make use of citation graphs (Garfield, 1955). This enables the use of graph- based algorithms like PageRank for determining re- searcher or paper centrality, and examining whether their influence grows or diminishes over time. However, because we are particularly interested in the change of ideas in a field over time, we have chosen a different method, following Kuhn (1962). In Kuhn’s model of scientific change, science pro- ceeds by shifting from one paradigm to another. Because researchers’ ideas and vocabulary are con- strained by their paradigm, successive incommensu- rate paradigms will naturally have different vocabu- lary and framing. Kuhn’s model is intended to apply only to very large shifts in scientific thought rather than at the micro level of trends in research foci. Nonetheless, we propose to apply Kuhn’s insight that vocabulary and vocabulary shift is a crucial indicator of ideas and shifts in ideas. Our operationalization of this in- sight is based on the unsupervised topic model La- tent Dirichlet Allocation (LDA; Blei et al. (2003)). For many fields, doing this kind of historical study would be very difficult. Computational linguistics has an advantage, however: the ACL Anthology, a public repository of all papers in the Computational Linguistics journal and the conferences and work- shops associated with the ACL, COLING, EMNLP, and so on. The ACL Anthology (Bird, 2008), and comprises over 14,000 documents from conferences …

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