Full-text search and indexing
– Full-text search is divided into indexing and searching when dealing with a large number of documents or substantial search queries.
– The indexing stage scans the text of all documents and builds a list of search terms (index).
– Stop words, common and meaningless words, are ignored during indexing.
– Language-specific stemming is used to record words with similar concepts under a single index entry.
Precision vs. recall tradeoff
– Recall measures the quantity of relevant results returned by a search, while precision measures the quality of the results.
– Low-precision, low-recall search results in a small number of relevant results returned.
– Full-text search systems use options like stop words and stemming to increase precision and recall.
– Controlled-vocabulary searching helps eliminate ambiguities and improve precision.
– There is a trade-off between precision and recall: increasing precision may lower recall and vice versa.
– Full-text searching often retrieves irrelevant documents, called false positives.
– False positives are caused by the inherent ambiguity of natural language.
– Clustering techniques based on Bayesian algorithms can reduce false positives.
– Clustering categorizes documents based on relevant words, improving search results.
– This technique is extensively used in the e-discovery domain.
Performance improvements and improved querying tools
– Full text searching deficiencies are addressed by providing users with improved querying tools.
– Keywords improve recall by including synonyms of words that describe the subject.
– Field-restricted search limits searches to a specific field within a data record.
– Boolean queries using operators like AND, NOT, and OR increase precision.
– Phrase search matches documents containing a specified phrase.
– Concept search matches multi-word concepts, such as compound term processing.
– Concordance search produces an alphabetical list of principal words with their context.
– Proximity search matches documents with words separated by a specified number of words.
– Regular expression employs a complex querying syntax for precise retrieval conditions.
– Fuzzy search looks for documents that match given terms with some variation around them.
Software and references
– Thunderstone Software LLC
– [Other software products for full-text indexing and searching]
– In practice, it may be difficult to determine how a given search engine works.
– The search algorithms employed by web-search services are seldom fully disclosed.
– Capabilities of Full Text Search System (Archived from the original on December 23, 2010)
– Coles, Michael (2008). Pro Full-Text Search in SQL Server 2008 (Version 1ed.). Apress Publishing Company. ISBN978-1-4302-1594-3.
– B., Yuwono; Lee, D. L. (1996). Search and ranking algorithms for locating resources on the World Wide Web. 12th International Conference on Data Engineering (ICDE96). p.164.
This article needs additional citations for verification. (August 2012)
In text retrieval, full-text search refers to techniques for searching a single computer-stored document or a collection in a full-text database. Full-text search is distinguished from searches based on metadata or on parts of the original texts represented in databases (such as titles, abstracts, selected sections, or bibliographical references).
In a full-text search, a search engine examines all of the words in every stored document as it tries to match search criteria (for example, text specified by a user). Full-text-searching techniques appeared in the 1960s, for example IBM STAIRS from 1969, and became common in online bibliographic databases in the 1990s.[verification needed] Many websites and application programs (such as word processing software) provide full-text-search capabilities. Some web search engines, such as the former AltaVista, employ full-text-search techniques, while others index only a portion of the web pages examined by their indexing systems.
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