2021/07/02 by Abheesht Sharma, Gunjan Chhablani, Sharma, Abheesht +5
Computer Science · Biochemistry, Genetics and Molecular Biology · #Advanced Text Analysis Techniques #Topic Modeling #Biomedical Text Mining and Ontologies
paper · pdf · doi:10.48550/arxiv.2107.01198
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.