DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS

Research in analysis of big scholarly data has increased in the recent past and it aims to understand research dynamics and forecast research trends. The ultimate objective in this research is to design and implement novel and scalable methods for extracting knowledge and computational history. While citations are highly used to identify emerging/rising research topics, they can take months or even years to stabilise enough to reveal research trends. Consequently, it is necessary to develop faster yet accurate methods for trend analysis and computational history that dig into content and semantics of an article. Therefore, this paper aims to conduct a fine-grained content analysis of scientific corpora from the domain of {\it Machine Learning}. This analysis uses {DeepHist, a deep learning-based computational history approach; the approach relies on a dynamic word embedding that aims to represent words with low-dimensional vectors computed by deep neural networks. The scientific corpora come from 5991 publications from Neural Information Processing Systems (NIPS) conference between 1987 and 2015 which are divided into six $5$-year timespans. The analysis of these corpora generates visualisations produced by applying t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction. The qualitative and quantitative study reported here reveals the evolution of the prominent Machine Learning keywords; this evolution supports the popularity of current research topics in the field. This support is evident given how well the popularity of the detected keywords correlates with the citation counts received by their corresponding papers: Spearman's positive correlation is 100%. With such a strong result, this work evidences the utility of deep learning techniques for determining the computational history of science