Editing Information retrieval (section)

=== Third Dimension: representational approach-based classification ===
In addition to the theoretical distinctions, modern information retrieval models are also categorized on how queries and documents are represented and compared, using a practical classification distinguishing between sparse, dense and hybrid models.<ref name=":02">{{cite arXiv | eprint=2107.09226 | last1=Kim | first1=Dohyun | last2=Zhao | first2=Lina | last3=Chung | first3=Eric | last4=Park | first4=Eun-Jae | title=Pressure-robust staggered DG methods for the Navier-Stokes equations on general meshes | date=2021 | class=math.NA }}</ref>

* '''''Sparse''''' models utilize interpretable, term-based representations and typically rely on inverted index structures. Classical methods such as TF-IDF and BM25 fall under this category, along with more recent learned sparse models that integrate neural architectures while retaining sparsity.<ref name=":6">{{cite arXiv | eprint=2104.08663 | last1=Thakur | first1=Nandan | last2=Reimers | first2=Nils | last3=Rücklé | first3=Andreas | last4=Srivastava | first4=Abhishek | last5=Gurevych | first5=Iryna | title=BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models | date=2021 | class=cs.IR }}</ref>
* '''''Dense''''' models represent queries and documents as continuous vectors using deep learning models, typically transformer-based encoders. These models enable semantic similarity matching beyond exact term overlap and are used in tasks involving semantic search and question answering.<ref>{{Cite journal |last1=Lau |first1=Jey Han |last2=Armendariz |first2=Carlos |last3=Lappin |first3=Shalom |last4=Purver |first4=Matthew |last5=Shu |first5=Chang |date=2020 |editor-last=Johnson |editor-first=Mark |editor2-last=Roark |editor2-first=Brian |editor3-last=Nenkova |editor3-first=Ani |title=How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context |url=https://aclanthology.org/2020.tacl-1.20/ |journal=Transactions of the Association for Computational Linguistics |volume=8 |pages=296–310 |doi=10.1162/tacl_a_00315}}</ref>
* '''''Hybrid''''' models aim to combine the strengths of both approaches, integrating lexical (tokens) and semantic signals through score fusion, late interaction, or multi-stage ranking pipelines.<ref>{{cite arXiv | eprint=2109.10739 | last1=Arabzadeh | first1=Negar | last2=Yan | first2=Xinyi | last3=Clarke | first3=Charles L. A. | title=Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection | date=2021 | class=cs.IR }}</ref>

This classification has become increasingly common in both academic and the real world applications and is getting widely adopted and used in evaluation benchmarks for Information Retrieval models.<ref name=":12">{{cite arXiv | eprint=2010.06467 | last1=Lin | first1=Jimmy | last2=Nogueira | first2=Rodrigo | last3=Yates | first3=Andrew | title=Pretrained Transformers for Text Ranking: BERT and Beyond | date=2020 | class=cs.IR }}</ref><ref name=":6" />