Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 249–253 (2019)
Abstract. Highly structured knowledge bases such as lexical semantic networks contain various connectivity patterns that can be learned as node features using dedicated frameworks. However, semantic relations are often unequally distributed over such knowledge resources. Some of the language partitions may benefit from integrating structured resources which are more easily available for resource-rich languages. In the present paper, we propose a simple endogenous method for enhancing a multilingual knowledge base through the cross-lingual semantic relation inference. It can be run on multilingual resources prior to semantic representation learning. Multilingual knowledge bases may integrate preexisting structured resources available for resource-rich languages. We aim at performing cross-lingual inference on them to improve the low resource language by creating semantic relationships.
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