1. Andrey K., M. Dorgham, O. Oliynyk, C. Biemann, and A. Panchenko. 2019. Learning graph embeddings from WordNet-based similarity measures. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), Minneapolis, Minnesota, 125–15. Association for Computational Linguistics. https://aclanthology.org/S19-1014/
2. Anna G., D. Aleksandr, and M. Satoshi. 2016. Analogy-based detection of morphological and semantic relations with word embeddings: What works and what doesn’t. In Proceedings of the NAACL Student Research Workshop, San Diego, California, 8–15. Association for Computational Linguistics.
3. Cao, S., W. Lu, and Q. Xu. 2015. GraRep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM ’15), 891–900, (NY), NY, USA: Association for Computing Machinery.
4. Deep Neural Networks for Learning Graph Representations
5. Electronics and Telecommunications Research Institute. 2017. Word Sense Disambiguation from Electronics and Telecommunications Research Institute. Retrieved March 4 2022 from https://aiopen.etri.re.kr/guide/Word