Information Retrieval and Semantic NLP Models
Explore the exact ranking equations, neural encoders, and chunk scoring models that drive search query relevancy.
TF-IDF (Term Frequency-Inverse Document Frequency)
Word importance and relative frequency inside a specific page compared to a wider corpus scale.
Helps establish baseline word prominence and highlights keywords that are statistically central to the page compared to generic filler words.
Document raw text string.
Normalized numerical keyword importance indices table.
Okapi BM25
LEXICAL keyword overlap matching score, incorporating document length normalization.
Underpins standard open-source search engine indexing models (Elasticsearch, Lucene) and models Google's initial search document pooling.
Document body text + target keyword query.
Numerical relevance score representing lexical match quality.
Cosine Lexical Similarity
Normalized directional angle cosine between two bag-of-words vectors representing overlapping terms.
Calculates pure keyword overlap speed without needing computationally heavy vector model overlays.
Two distinct body texts or a text and an H1 header.
Lexical similarity percentage (0% to 100%).
Named Entity Recognition (NER)
Extraction and classification of unstructured words into standardized entity classes (People, Organizations, Locations, Dates).
Builds the primary semantic vocabulary for search engine indexing. Search engines parse recognized entities to identify subject domains.
Document raw text or page HTML.
Classified entities list + visual markups.
POS Tagging & Dependency Parsing
Linguistic word classes (Nouns, Verbs, Modifiers) and structural syntax dependency trees.
Helps NLP engines analyze sentence patterns, voice, readability, and isolates the grammatical subject to calculate entity salience.
Clean sentence string.
POS distribution tables and syntactic dependencies.
BERT (MS MARCO Reranker)
Deep contextual conceptual relevance of article passages directly aligning with user search intents.
BERT reranks initial search results based on true conversational context rather than exact keyword overlap spelling.
User query + paragraph string.
Deep semantic relevance percentage score.
Sentence Transformers (SBERT)
High-dimensional vector overlap of sentences to measure similarity of meaning.
Identifies duplicate paragraphs, maps argument progression cohesion, and alerts writers to redundant conceptual circles.
Paragraph block inputs.
Cosine vector meaning overlap percentage.
RAG Chunk Optimization & Passage Ranking
Text block quality, size bounds, and semantic density for prompt injection readiness.
Determines whether generative search systems (Perplexity, SearchGPT) can easily extract and cite your page in AI answers.
Full document structure + HTML headers.
AI answer readiness score + specific structuring suggestions.