tekstindexen

Tekstindexen are data structures used to enable fast searching in large collections of text by mapping terms to the documents that contain them. The most common form is the inverted index, which allows a query to be turned into a set of candidate documents quickly. Text indexing underpins full-text search, phrase queries, and ranking in many information retrieval systems.

A typical inverted index consists of a dictionary (the term lexicon) and postings lists. For each term, the postings list records which documents contain the term, and often how many times it occurs (term frequency) and the positions of occurrences (for exact phrase and proximity queries). Variants include positional indexes, which store positions to support exact phrase matching, and biword or other n-gram indexes that facilitate faster phrase or proximity queries on large scales.

Indexing typically involves preprocessing the text: normalization, tokenization, lowercasing, removal of stop words, and stemming or lemmatization. The resulting tokens are added to the index, with occurrences recorded. Indexes can be stored on disk with compression and may be kept in memory for speed, depending on size and latency requirements. Updates can be handled incrementally or require periodic reindexing.

Searching and ranking use the index to retrieve candidate documents and then order them by relevance. Common ranking methods include TF-IDF, BM25, and language-model based approaches, sometimes augmented with positional information, document length normalization, or learning-to-rank techniques.

Applications include search engines, content management systems, digital libraries, and e-commerce platforms. Key challenges are managing index size, supporting multilingual text, handling synonyms and ambiguity, keeping indices up to date in dynamic collections, and balancing speed with accuracy.