NLPExplorer: Exploring the Universe of NLP Papers

TL;DR

NLPExplorer is an automated platform that indexes, visualizes, and analyzes NLP research papers, authors, and topics, providing insights into trends, popular datasets, and influential works to aid researchers.

Contribution

It introduces a manually curated, multi-faceted categorization of NLP research and offers novel features like author popularity, dataset trends, and accessible datasets via API.

Findings

Identifies trending NLP topics and datasets over time

Highlights influential authors and papers in NLP

Provides accessible datasets for future research

Abstract

Understanding the current research trends, problems, and their innovative solutions remains a bottleneck due to the ever-increasing volume of scientific articles. In this paper, we propose NLPExplorer, a completely automatic portal for indexing, searching, and visualizing Natural Language Processing (NLP) research volume. NLPExplorer presents interesting insights from papers, authors, venues, and topics. In contrast to previous topic modelling based approaches, we manually curate five course-grained non-exclusive topical categories namely Linguistic Target (Syntax, Discourse, etc.), Tasks (Tagging, Summarization, etc.), Approaches (unsupervised, supervised, etc.), Languages (English, Chinese,etc.) and Dataset types (news, clinical notes, etc.). Some of the novel features include a list of young popular authors, popular URLs, and datasets, a list of topically diverse papers and recent popular papers. Also, it provides temporal statistics such as yearwise popularity of topics, datasets, and seminal papers. To facilitate future research and system development, we make all the processed datasets accessible through API calls. The current system is available at http://lingo.iitgn.ac.in:5001/