EMNLP Workshop on Deep Learning Inside Out

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

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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.


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InProceedings

https://arxiv.org/abs/2010.05732

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