NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature
This tool helps NLP researchers and practitioners efficiently navigate and analyze the growing body of literature, though it is incremental as it builds on existing data sources and visualization techniques.
The authors tackled the challenge of exploring and analyzing the NLP literature by creating a unified dataset from the ACL Anthology and Google Scholar, and developed interactive visualizations that allow users to filter and search papers by various criteria, such as time periods and authors.
As part of the NLP Scholar project, we created a single unified dataset of NLP papers and their meta-information (including citation numbers), by extracting and aligning information from the ACL Anthology and Google Scholar. In this paper, we describe several interconnected interactive visualizations (dashboards) that present various aspects of the data. Clicking on an item within a visualization or entering query terms in the search boxes filters the data in all visualizations in the dashboard. This allows users to search for papers in the area of their interest, published within specific time periods, published by specified authors, etc. The interactive visualizations presented here, and the associated dataset of papers mapped to citations, have additional uses as well including understanding how the field is growing (both overall and across sub-areas), as well as quantifying the impact of different types of papers on subsequent publications.