What is Latent Semantic Indexing (LSI)
Latent Semantic Indexing (LSI) - Concept-based retrieval
A technique that uses singular value decomposition to identify patterns in the relationships between terms and concepts in unstructured data.
How It Works
LSI analyzes relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. It reduces the dimensionality of the data, capturing the underlying structure in the data.
Technical Details
LSI uses singular value decomposition (SVD) to decompose the term-document matrix into three matrices, capturing the most important relationships. This process reduces noise and reveals the latent semantic structure.
Best Practices
- Use LSI for concept-based retrieval
- Combine with other retrieval techniques
- Implement efficient computation pipelines
- Regularly update document collections
- Monitor LSI performance
Common Pitfalls
- Ignoring document collection updates
- Over-relying on LSI alone
- Inefficient computation pipelines
- Poor performance monitoring
- Lack of comprehensive analysis
Advanced Tips
- Use hybrid retrieval techniques
- Implement LSI optimization
- Consider domain-specific adjustments
- Optimize for specific use cases
- Regularly review LSI performance