On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling

Autor:	Patel, Rajat, Ferraro, Francis
Rok vydání:	2020
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work also shows that jointly modeling both structured knowledge tuples and language improves both. Comment: To appear at the EMNLP 2020 Workshop on Deep Learning Inside Out
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.05732 Zobrazit plný text záznamu View this record from Arxiv