2018
ACL
ACL 2018
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
Abstract
AbstractThis paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural information retrieval. Our experiments on a commercial search log demonstrate the effectiveness of EDRM. Our analyses reveal that knowledge graph semantics significantly improve the generalization ability of neural ranking models.
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Science and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— entity ranking
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Hot Topic Early Bird
— semantic search
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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, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Applications > Information Retrieval
Computer Science > Applications > Information Retrieval
Artificial Intelligence > Core AI > Knowledge Graph
Deep Learning > Learning Types > Multi-Modal Learning