Published November 10, 2025 | Version v1
Preprint Open

Mathematical Foundations of Text Vectorization and the Sentence-BERT Algorithm (understanding SEO content analysis)

  • 1. IncRev SEO Research

Description

This document presents a concise, math-forward background to vectorizing text into vectors (embeddings), explains why cosine similarity is a principled measure of semantic relatedness, and adds a dedicated section on “The math behind the Sentence-BERT algorithm.”
The goal is to connect linear-algebraic derivations (e.g., PMI factorization) with modern transformer-based encoders used in practical SEO workflows. 
It also gives a brief overview of practical impact on modern SEO work like Topical authority, content clustering, internal linking and optimizing relevance for backlinks. 

In the end we also have a short introduction to IncRevs own Semantic Tool called "QueryMatch". This tool will be presented in its own research papers and case studies.

Files

Mathematical Foundations of Text Vectorization and the Sentence-BERT Algorithm – David Vesterlund at IncRev SEO Research Community (DOI- 10.5281:zenodo.17570412).pdf

Additional details

Related works

Is published in
Other: https://independent.academia.edu/DavidVesterlund (Other)
Is supplement to
Preprint: 10.5281/zenodo.17360293 (DOI)

Dates

Issued
2025-11-10

References