2020
EMNLP
EMNLP 2020
On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling
Abstract
AbstractWe 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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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Knowledge & Reasoning and Natural Language Processing
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Keyword Pioneer
— entity type prediction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio