How deeply does Elasticsearch in Action, Second Edition cover inverse document frequency?

Defines IDF through collection-wide term occurrence and exposes its score calculation, while deferring customized similarity algorithms.

Inverse document frequency

As explained in Elasticsearch in Action, Second Edition

Inverse document frequency measures how uncommon a term is across a document collection, giving rarer terms greater weight.

Inverse document frequency (IDF) measures whether a search term is common or rare across an indexed collection. In term frequency–inverse document frequency (TF-IDF), a term that appears often in one document but rarely across the collection receives more relevance weight. section 3.4.2

IDF contributes to relevance alongside other signals:

Term frequency (TF) measures occurrences within a document; higher frequency can increase relevance. section 3.4.2
Document frequency reflects collection-wide occurrence; higher document frequency lowers relevance under TF-IDF. section 3.4.2
BM25 improves on TF-IDF by limiting repeated-term effects and normalizing for document length. section 3.4.2

Inspect IDF in a movie search score

Run a match query with "explain": true to expose scoring details. For a search such as title: "Lord", the explanation identifies how many documents contain the term (n), the total document count (N), and the IDF contribution to the score. section 8.8.3

Listing 8.20 Asking the engine to explain the score

GET movies/_search
{
  "explain": true, 
  "_source": false, 
  "query": {
    "match": {
      "title": "Lord"
    }
  }
}

Use the explain-enabled search request to inspect the engine’s score calculation instead of treating relevance as a black box. section 8.8.3

IDF is one component of Elasticsearch relevance scoring. Similarity algorithms apply scoring per field, and BM25 is the default algorithm; alternative or customized similarity choices are available when default ranking does not fit the requirement. section 3.4.2 section 3.4.1

How Elasticsearch in Action, Second Edition covers it

Cover of Elasticsearch in Action, Second Edition

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Also explained in

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Natural Language Processing in Action, Second EditionInverse document frequency is a term-weighting measure defined from how many documents in a corpus contain a term, assigning lower weight to terms occurring in many documents.
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AI Agents in Action, Second EditionInverse document frequency is a logarithmic measure of how uncommon a term is across a document collection.
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AI-Powered SearchInverse document frequency is a term-weighting measure that assigns greater weight to terms appearing in fewer documents and lower weight to common terms.

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Related concepts

term frequency-inverse document frequencypart of · Term frequency-inverse document frequency is a text-weighting scheme that combines a term’s frequency in a document with its inverse frequency across a document collection.
relevanceuses · Relevance is the degree to which information, a result, or a response pertains to a query, task, purpose, or information need.
BERTScoreused by · BERTScore is an automatic text-generation evaluation metric that compares candidate and reference texts using contextual representations of their tokens.
BM25used by · BM25 is a probabilistic information-retrieval ranking function that scores documents for a query using term frequency, inverse document frequency, and document-length normalization.
ranking modelused by · A ranking model is a predictive model that scores or orders candidate items according to their estimated relevance, preference, or utility.

This explainer was generated by Manning's knowledge graph from the text of Elasticsearch in Action, Second Edition. The section links point to the parts of the book it draws on.