Graph-based NLP
From Language and Information Technologies
Jump to: navigation, search
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.
M. Peters, M. Neumann, R. Logan, R. Schwartz, V. Joshi, S. Singh, and N. Smith. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), page 43--54. Hong Kong, China, Association for Computational Linguistics, (November 2019)
A. Bosselut, H. Rashkin, M. Sap, C. Malaviya, A. Celikyilmaz, and Y. Choi. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, page 4762--4779. Florence, Italy, Association for Computational Linguistics, (July 2019)
Z. Zhang, X. Han, Z. Liu, X. Jiang, M. Sun, and Q. Liu. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, page 1441--1451. Florence, Italy, Association for Computational Linguistics, (July 2019)