Distance Based Korean WordNet(alias. KorLex) Embedding Model

Author:

Park SeongReol1,Shin JoongMin1,Cho Sanghyun1,Kwon Hyuk-Chul1,Lee Jung-Hun2

Affiliation:

1. Department of Information Convergence Engineering, Pusan National University, Busan, South Korea

2. Department of Artificial Intelligence, Dong-Eui University, Busan, South Korea

Funder

Innovative Human Resource Development for Local Intellectualization program

Institute of Information & Communications Technology Planning & Evaluation

Korean government

Publisher

Informa UK Limited

Reference22 articles.

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

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