Retrieval Techniques | EntropyObserver

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Representative Retrieval Techniques

Technique	Core Concept	Typical Use Cases
Boolean Retrieval	Exact matching using Boolean logic (`AND`, `OR`, `NOT`) on term presence.	Legal, medical, or expert systems requiring strict control.
Vector Space Model (VSM)	Represents documents and queries as weighted term vectors (e.g., using TF-IDF).	Classic academic retrieval systems; early search engines.
BM25	An enhanced probabilistic ranking model that adjusts for term frequency and document length.	Widely used in modern search engines; strong baseline for ranking.
Neural Retrieval (Neural IR)	Leverages deep learning (e.g., BERT, DPR) to model semantic similarity beyond exact words.	QA systems, semantic search, large-scale web search.
Hybrid Retrieval	Combines traditional (e.g., BM25) and neural methods for improved relevance and recall.	State-of-the-art search engines and intelligent assistants.

Comparison of Retrieval Methods

Method	Matching Strategy	Ranking Support	Semantic Understanding	Complexity
Boolean Retrieval	Exact term matching	❌ No	❌ No	Low (Simple)
Vector Space Model	TF-IDF + Cosine similarity	✅ Yes	❌ No	Medium
BM25	Enhanced TF-IDF scoring	✅ Yes	❌ No (but high effectiveness)	Medium
Neural Retrieval	Semantic embeddings (e.g., BERT)	✅ Yes	✅ Yes	High (Complex)

Key Takeaways

Boolean Retrieval is fast and deterministic, but lacks ranking and fuzziness.

VSM and BM25 introduced ranking and weighting, significantly improving user experience.

Neural IR adds semantic understanding, allowing queries like "Who wrote Hamlet?" to return documents mentioning Shakespeare.

Hybrid Retrieval is now best practice, blending the precision of BM25 with the flexibility of neural models.

Boolean Retrieval Overview

Boolean retrieval uses binary vectors to represent the presence or absence of terms in documents—1 if the term occurs, 0 if it does not. Queries are answered by applying Boolean logic operators: AND, OR, and NOT.

Boolean Logic

The AND operation returns 1 only if both inputs are 1.

The OR operation returns 1 if either input is 1.

The NOT operation simply flips the bit—turning 1 into 0 and 0 into 1. These operations allow us to combine term vectors and identify documents that satisfy complex query conditions.

Term-Document Matrix Example

Consider three documents: