Enriching Hindi WordNet Using Knowledge Graph Completion Approach

Abstract

Even though the use of WordNet in the Natural Language Processing domain is unquestionable, creating and maintaining WordNet is a cumbersome job and it is even difficult for low resource languages like Hindi. In this study, we aim to enrich the Hindi WordNet automatically by using state-of-the-art knowledge graph completion (KGC) approaches. We pose the automatic Hindi WordNet enrichment problem as a knowledge graph completion task and therefore we modify the WordNet structure to make it appropriate for applying KGC approaches. Second, we attempt five KGC approaches of three different genres and compare the performances for the task. Our study shows that ConvE is the best KGC methodology for this specific task compared to other KGC approaches.

Cite

@inproceedings{awale2022hindiwordnet,
  author = {Awale, Sushil and Jana, Abhik},
  title = {Enriching {Hindi} {WordNet} Using Knowledge Graph Completion Approach},
  booktitle = {Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia co-located with the Language Resources and Evaluation Conference, {EURASIA}@{LREC} 2022, Marseille, France, June 20, 2022},
  pages = {81--85},
  publisher = {European Language Resources Association},
  year = {2022},
  url = {https://aclanthology.org/2022.eurali-1.13},
  month = jun,
}