Year: 2024
Type: article
Abstract: 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.
Source: Applied Artificial Intelligence
Institutions: Pusan National University, Dong-Eui University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,195
Awards: G4246929629, G518669462, G6949731491
OpenAlex ID: W4402527665