SensEmbed is a knowledge-based approach for obtaining continuous representations for individual word senses proposed by Ignacio Iacobacci, Mohammad Taher Pilehvar and Roberto Navigli. We propose a multi-faceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement.
| 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. |
