What is latent semantic indexing, how does Google use it, and how understanding this core functionality of the Google algorithm will help you create better content.

1. Semantics & Science(and a tiny bit of statistics)
State of Search 2017

2. Introduction
Upasna Gautam
aka
Pas
Director of Digital Strategy at Geek Powered Studios
@UpasnaGautam

3. Objectives
• What is semantic search?
• How does Google make it work?
• How can you make it work?

4. SEO: Then &
Now

5. SEO: Then & Now
Back then:
• Keyword-focused:
• Text retrieval system relied on exact match keywords
• Weighed documents by keyword frequency
• Unable to distinguish synonyms and homographs
• Synonym: Words that share the same meaning (e.g. car
and automobile)
• Homograph: More than one meaning depending on
context (e.g. “charge)

6. SEO: Then & Now
Now:
• Driven by intent and context
• Provide relevant answers to complex and vague
queries

7. SEO: Then & Now

8. SEO: Then & Now
Now:
• “best vegan tacos dallas”
• “late night texmex delivery
dallas”
• “best happy hour margaritas
dallas”

9. SEO: Then & Now
Search Experience Optimization
Now:

10. SEO: Then & Now
What enabled search engines to
understand our queries on an
intelligent level?

11. Latent
Semantic Indexing

12. Latent Semantic Indexing
Semantics:
A branch of linguistics that studies the
relationship between words and
sentences and their actual meanings.

13. Latent Semantic Indexing
Latent Semantic Indexing (LSI):
• Mathematical algorithm based on Singular Value
Decomposition (SVD)
• Text indexing and retrieval method
• How terms and concepts are related

14. Latent Semantic Indexing
Latent Semantic Indexing (LSI):
• LSI works by projecting a large multi-dimensional
space down into a smaller number of dimensions
• Semantically similar words get bunched together
• Boundary blurring allows LSI to go beyond exact
keyword matching

15. Latent Semantic Indexing

16. Latent Semantic Indexing
• LSI uses Singular Value Decomposition (SVD) to
decompose this matrix
• Preserves information about relative distances
between document vectors
• Collapsed into smaller dimensions
• Information is lost and words are superimposed on
one another

17. Latent Semantic Indexing
Isn’t information loss a BAD thing?

18. Latent Semantic Indexing
NOPE!
Reductive mapping is good!

19. SEO: Then & Now
• Noise reduction
• Reveal similarities that were latent
Similar terms become more similar, while
dissimilar things remain distinct

20. Key Takeaways

21. Key Takeaways
• Create consumer need states to define searcher
intent and context
• Create content that answers questions
• Optimize for topics and concepts, not just
keywords

22. The End