In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora. We open source an easy-to-use tool coined: DRIFT, which allows researchers to track research trends and development over the years. The analysis methods are collated from well-cited research works, with a few of our own methods added for good measure. Succinctly put, some of the analysis methods are: keyword extraction, word clouds, predicting declining/stagnant/growing trends using Productivity, tracking bi-grams using Acceleration plots, finding the Semantic Drift of words, tracking trends using similarity, etc. To demonstrate the utility and efficacy of our tool, we perform a case study on the cs.CL corpus of the arXiv repository and draw inferences from the analysis methods. The toolkit and the associated code are available here: https://github.com/rajaswa/DRIFT.
@article{arxiv.2107.01198,
title = {DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature},
author = {Abheesht Sharma and Gunjan Chhablani and Harshit Pandey and Rajaswa Patil},
journal= {arXiv preprint arXiv:2107.01198},
year = {2021}
}
Comments
Accepted at EMNLP-2021 (System Demonstration Track)