Applied Artificial Intelligence (Dec 2024)

Distance Based Korean WordNet(alias. KorLex) Embedding Model

  • SeongReol Park,
  • JoongMin Shin,
  • Sanghyun Cho,
  • Hyuk-Chul Kwon,
  • Jung-Hun Lee

DOI
https://doi.org/10.1080/08839514.2024.2398920
Journal volume & issue
Vol. 38, no. 1

Abstract

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The objective of this study was to create graph embedding vectors using Korean WordNet (KorLex) and apply them to neural network word-embedding models. Semantic knowledge, especially lexical semantic knowledge in a language, can be represented by word-embedding vectors or graph structures of lexical databases, such as WordNet. Both representations capture common semantics; however, some semantic knowledge is only captured in a specific way or not at all. In a previous study, Path2vec mapped WordNet graphs to graph-embedding vectors using similarity scores between two words. In this study, we propose two main approaches. First, we mapped the knowledge in the Korean lexical database KorLex onto graph-embedding vectors. We then applied these embedding vectors to deep neural network word embeddings to capture additional semantic knowledge in the Korean language. On a custom test set, the proposed approach improved performance by capturing additional semantic knowledge in similarity and analogy analyses. We plan to apply a variant of this to other deep neural embedding models.