# Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach

arXiv: https://arxiv.org/abs/2010.01195 · HTML: https://mlsanity.com/paper/2010.01195/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 30 of 32)

- [Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval](https://mlsanity.com/paper/2203.03367/) (2022)
- [UnifieR: A Unified Retriever for Large-Scale Retrieval](https://mlsanity.com/paper/2205.11194/) (2022)
- [Rethinking Search: Making Domain Experts out of Dilettantes](https://mlsanity.com/paper/2105.02274/) (2021)
- [LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval](https://mlsanity.com/paper/2208.13661/) (2022)
- [Graph RAG-Tool Fusion](https://mlsanity.com/paper/2502.07223/) (2025)
- [Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems](https://mlsanity.com/paper/2511.01854/) (2025)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization](https://mlsanity.com/paper/2510.05038/) (2025)
- [Lexically-Accelerated Dense Retrieval](https://mlsanity.com/paper/2307.16779/) (2023)
- [CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval](https://mlsanity.com/paper/2311.02834/) (2023)
- [Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis](https://mlsanity.com/paper/2504.05324/) (2025)
- [Multi-Field Adaptive Retrieval](https://mlsanity.com/paper/2410.20056/) (2024)
- [RAG Playground: A Framework for Systematic Evaluation of Retrieval Strategies and Prompt Engineering in RAG Systems](https://mlsanity.com/paper/2412.12322/) (2024)
- [Agent-as-a-Graph: Knowledge Graph-Based Tool and Agent Retrieval for LLM Multi-Agent Systems](https://mlsanity.com/paper/2511.18194/) (2025)
- [Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval](https://mlsanity.com/paper/2210.05521/) (2022)
- [Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence](https://mlsanity.com/paper/2104.09393/) (2021)
- [Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems](https://mlsanity.com/paper/2206.12993/) (2022)
- [MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval](https://mlsanity.com/paper/2302.11953/) (2023)
- [Identification of Regulatory Requirements Relevant to Business Processes: A Comparative Study on Generative AI, Embedding-based Ranking, Crowd and Expert-driven Methods](https://mlsanity.com/paper/2401.02986/) (2024)
- [Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain](https://mlsanity.com/paper/2409.01357/) (2024)
- [HEISIR: Hierarchical Expansion of Inverted Semantic Indexing for Training-free Retrieval of Conversational Data using LLMs](https://mlsanity.com/paper/2503.04141/) (2025)
- [Document Optimization for Black-Box Retrieval via Reinforcement Learning](https://mlsanity.com/paper/2604.05087/) (2026)
- [VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering](https://mlsanity.com/paper/2604.08549/) (2026)
- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)
- [Knowledge Models for Cancer Clinical Practice Guidelines : Construction, Management and Usage in Question Answering](https://mlsanity.com/paper/2407.21053/) (2024)
- [Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation](https://mlsanity.com/paper/2509.13603/) (2025)
- [Rationale-Augmented Retrieval with Constrained LLM Re-Ranking for Task Discovery](https://mlsanity.com/paper/2510.05131/) (2025)
- [AutoIndex: Learning Representation Programs for Retrieval](https://mlsanity.com/paper/2607.18603/) (2026)
- [Do Static Embeddings Add Value to Hybrid Dutch Retrieval?](https://mlsanity.com/paper/2608.02112/) (2026)
- [LINE Conversation History Retrieval for Personal Memory RAG: Evaluating Search Representations and Hybrid Retrieval](https://mlsanity.com/paper/2608.27809/) (2026)

## Abstract (arXiv)

Search engines often follow a two-phase paradigm where in the first stage (the retrieval stage) an initial set of documents is retrieved and in the second stage (the re-ranking stage) the documents are re-ranked to obtain the final result list. While deep neural networks were shown to improve the performance of the re-ranking stage in previous works, there is little literature about using deep neural networks to improve the retrieval stage. In this paper, we study the merits of combining deep neural network models and lexical models for the retrieval stage. A hybrid approach, which leverages both semantic (deep neural network-based) and lexical (keyword matching-based) retrieval models, is proposed. We perform an empirical study, using a publicly available TREC collection, which demonstrates the effectiveness of our approach and sheds light on the different characteristics of the semantic approach, the lexical approach, and their combination.