Graph-based NLP
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The goal of this research project is to investigate efficient graph-based representations of text, and explore the application of ranking models based on such graph structures to natural language processing tasks. We bring together methods from computational linguistics and graph-theory, and combine them into a suite of innovative approaches that will improve and ultimately solve difficult problems in natural language processing. Specifically, we are currently working on the application of graph centrality algorithms to problems such as word sense disambiguation, text summarization and keyword extraction.
So, a while ago, I’ve decided to code a library to plot some information I had. The idea was to create simple graphics in a way they would be easy to create, beautiful and good to present to people with no or few backgrounds on math and computers.
Graph mining refers to extracting knowledge from massive graphs. The data sets of telephone calls we see at AT&T can be viewed as a single graph, with several hundred million phone numbers as nodes, and calls between phone numbers as edges. It is a giant social network, like an internet connections graph or a rich citation network.
S. Wang, L. Hu, Y. Wang, X. He, Q. Sheng, M. Orgun, L. Cao, F. Ricci, and P. Yu. (2021)cite arxiv:2105.06339Comment: Accepted by IJCAI 2021 Survey Track, copyright is owned to IJCAI. The first systematic survey on graph learning based recommender systems. arXiv admin note: text overlap with arXiv:2004.11718.
N. Shao, Y. Cui, T. Liu, S. Wang, and G. Hu. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), page 7187--7192. Online, Association for Computational Linguistics, (November 2020)
M. Ryabinin, S. Popov, L. Prokhorenkova, and E. Voita. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), page 7317--7331. Online, Association for Computational Linguistics, (November 2020)