Co-occurrences

Co-occurrences measure how frequently terms appear together in texts, which algorithms use to build expectations about content completeness. When everyone in an industry writes 'SEO' alongside 'keywords' and 'links', this creates a generic vector; your content blends into the mass. Creating NEW co-occurrences like 'SEO' + 'citation probability' + 'retrieval score' yields higher TF-IDF scores (because IDF is high for rare combinations), delivering better scores in AI Search re-ranking.

Key principle: don't write like everyone else — unique term combinations distinguish content and build unique embeddings. A practical example: in a leasing article, instead of the standard set 'leasing + payment + car', add unique combinations like 'leasing + tax depreciation + residual value + WIBOR' that competitors don't use. Co-occurrences are the foundation of Distributional Semantics: words define each other through the contexts where they appear.

It's worth distributing unique co-occurrences evenly throughout the entire article, not just in the first paragraph, so that each chunk has a distinctive vector.