top of page
ABOUT
We propose a knowledge-based approach relying on the rich semantic information of BabelNet, for obtaining continuous representations for individual word senses
and by leveraging these representations and lexical-semantic knowledge, we put forward a semantic similarity measure with state-of-the-art performance on multiple datasets
DOWNLOADS
REFERENCES
Ignacio Iacobacci, Mohammad Taher Pilehvar and Roberto Navigli, SensEmbed: Learning Sense Embeddings for Word and Relational Similarity. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (ACL-IJCNLP 2015), Beijing, China, July 26-31, 2015.
​
Contact
Ignacio Iacobacci
Mohammad Taher Pilehvar
iacobacci [at] di.uniroma1 [dot] it
mp792 [at] cam [dot] ac [dot] uk
Roberto Navigli
navigli [at] di.uniroma1 [dot] it
CONTACT
bottom of page