About Us
Welcome to the Natural Language Processing Knowledge Graph (NLP-KG).
NLP-KG is a research project, aiming to provide comprehensive support for researchers engaged in the search and exploration of academic papers within the field of Natural Language Processing (NLP). Equipped with advanced features, this application was developed by researchers and explicitly tailored to the needs of the academic community.
Meet the Team
Our dedicated team works tirelessly to ensure that you have the best experience on our website. Get to know the individuals who contribute to making this platform great.
Tim Schopf
Researcher
Prof. Dr. Florian Matthes
Principal Investigator
Ferdy Hadiwijaya
Developer
Ronald Ernst
Developer
Patrick Kufner
Developer
Cansu Doğanay
Developer
Contact Us
Have questions, suggestions, or just want to say hello? Feel free to contact us. We appreciate your feedback and look forward to hearing from you!
Create an issue on GitHub or write an e-mail to tim.schopf@tum.de.
Frequently Asked Questions (FAQs)
Citation Information
You can find more details in our ACL 2024 Paper NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing. When citing our work in academic papers and theses, please use the following BibTeX entry:
@inproceedings{schopf-matthes-2024-nlp,
title = "{NLP}-{KG}: A System for Exploratory Search of Scientific Literature in Natural Language Processing",
author = "Schopf, Tim and
Matthes, Florian",
editor = "Cao, Yixin and
Feng, Yang and
Xiong, Deyi",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-demos.13",
pages = "127--135",
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.",
}