Studying the History of Ideas Using Topic Models

How can the development of ideas in a scientific field be studied over time? We apply 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 probabilistic methods starting in 1988, a steady increase in applications, and a sharp decline of research in semantics and understanding between 1978 and 2001, possibly rising again

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: David Hall, Daniel Jurafsky, Christopher D. Manning
• Keywords: EMNLP 2008, Latent Dirichlet Allocation, Natural Language Processing, Topic Clusters, Unsupervised Topic Modeling