Neural Information Retrieval Model

A Neural Information Retrieval Model is an information retrieval model that uses neural networks to learn semantic representations for matching queries with documents through deep learning methods.

• AKA: Neural IR Model, Deep Learning Information Retrieval Model, Neural Search Model.
• Context:
  • It can typically learn Query-Document Interactions with attention mechanisms.
  • It can typically capture Semantic Relationships through distributed representations.
  • It can typically optimize Relevance Functions with gradient-based learning.
  • It can often employ Pre-Trained Language Models for initialization.
  • It can often utilize Transfer Learning for domain adaptation.
  • It can often apply Multi-Task Learning for joint optimization.
  • It can often integrate Knowledge Distillation for model compression.
  • It can range from being a Representation-Focused Neural Retrieval Model to being a Interaction-Focused Neural Retrieval Model, depending on its matching paradigm.
  • It can range from being a Early-Fusion Neural Retrieval Model to being a Late-Fusion Neural Retrieval Model, depending on its combination strategy.
  • It can range from being a Single-Tower Neural Retrieval Model to being a Multi-Tower Neural Retrieval Model, depending on its encoder architecture.
  • It can range from being a Supervised Neural Retrieval Model to being a Self-Supervised Neural Retrieval Model, depending on its training approach.
  • ...
• Examples:
  • Architecture-Based Neural Retrieval Models, such as:
    • Bi-Encoder Model, Cross-Encoder Model, Poly-Encoder Model.
  • Method-Based Neural Retrieval Models, such as:
    • DSSM Model, DRMM Model, KNRM Model.
  • Domain-Specific Neural Retrieval Models, such as:
    • Legal Embedding Model, Medical Neural Retrieval Model, E-Commerce Neural Retrieval Model.
  • ...
• Counter-Examples:
  • Classical Retrieval Model, which uses statistical rather than neural methods.
  • Boolean Retrieval Model, which uses exact matching rather than learned representations.
  • Vector Space Model, which uses static rather than learned similarities.
• See: Bi-Encoder Model, Cross-Encoder Model, Dense Retrieval Method, Neural Network, Information Retrieval Model, Deep Learning, Semantic Search.

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