NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing

TL;DR

NLP-KG is an exploratory search system for scientific literature in NLP that combines semantic search, survey identification, hierarchical field visualization, and a chat interface for answering questions grounded in research publications.

Contribution

It introduces a comprehensive system integrating multiple exploration tools for NLP literature, enhancing user understanding and discovery beyond keyword searches.

Findings

Supports exploration of NLP research fields

Enables identification of survey papers and related areas

Provides a question-answering chat grounded in scientific knowledge

Abstract

Scientific literature searches are often exploratory, whereby users are not yet familiar with a particular field or concept but are interested in learning more about it. However, existing systems for scientific literature search are typically tailored to keyword-based lookup searches, limiting the possibilities for exploration. We propose NLP-KG, a feature-rich system designed to support the exploration of research literature in unfamiliar natural language processing (NLP) fields. In addition to a semantic search, NLP-KG allows users to easily find survey papers that provide a quick introduction to a field of interest. Further, a Fields of Study hierarchy graph enables users to familiarize themselves with a field and its related areas. Finally, a chat interface allows users to ask questions about unfamiliar concepts or specific articles in NLP and obtain answers grounded in knowledge retrieved from scientific publications. Our system provides users with comprehensive exploration possibilities, supporting them in investigating the relationships between different fields, understanding unfamiliar concepts in NLP, and finding relevant research literature. Demo, video, and code are available at: https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp.