Full text search

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In text retrieval, full text search (also called free search text) refers to a technique for searching a computer-stored document or database; in a full text search, the search engine examines all of the words in every stored document as it tries to match search words supplied by the user. Full-text searching techniques became common in online bibliographic databases in the 1970s. Most Web sites and application programs (such as word processing software) provide full text search capabilities. Some Web search engines, such as AltaVista employ full text search techniques, while others index only a portion of the Web pages examined by its indexing system.¹

The most common approach to full text search is to generate a complete index or concordance for all of the searchable documents. For each word (excepting stop words which are too common to be useful) an entry is made which lists the exact position of every occurrence of it within the database of documents. From such a list it is relatively simple to retrieve all the documents that match a query, without having to scan each document. Although for very small document collections full-text searching can be done by serial scanning, indexing is the preferred method for almost all full-text searching.

1 The false positive problem
2 The precision vs. recall tradeoff
3 Improving the performance of full text searching
- 3.1 Improved querying tools
- 3.2 Improved search algorithms
4 Notes
5 See also

[edit] The false positive problem

As anyone who has performed a free text search will readily recognize, free text searching is likely to retrieve many documents that are not relevant to the intended search question. Such documents are called false positives. The retrieval of irrelevant documents is often caused by the inherent ambiguity of natural language; for example, the word football might refer either to soccer, American, Canadian, Gaelic or Australian rules football, etc., whereas the person searching is probably interested in only one of these.

[edit] The precision vs. recall tradeoff

Due to the ambiguities of natural language, a full text search typically produces a retrieval list that has low precision: most of the items retrieved are irrelevant. Controlled-vocabulary searching solves this problem by tagging the documents in such a way that the ambiguities are eliminated. However, a controlled vocabulary search may have low recall: it may fail to retrieve some documents that are actually relevant to the search question. Despite the presence of many irrelevant documents in a free text search's retrieval list, a free text search may be able to locate a document that a controlled vocabulary search failed to retrieve.

[edit] Improving the performance of full text searching

The deficiencies of free text searching have been addressed in two ways: By providing users with tools that enable them to express their search questions more precisely, and by developing new search algorithms that improve retrieval precision.

[edit] Improved querying tools

Keywords. Document creators (or trained indexers) are asked to supply a list of words that describe the subject of the text, including synonyms of words that describe this subject. Keywords improve recall, particularly if the keyword list includes a search word that is not in the document text.
Field-restricted search. Some search engines enable users to limit free text searches to a particular field within a stored data record, such as "Title" or "Author."
Boolean queries. Searches that use Boolean operators (for example, "encyclopedia" AND "online" NOT "Encarta") can dramatically increase the precision of a free text search. The AND operator says, in effect, "Do not retrieve any document unless it contains both of these terms." The NOT operator says, in effect, "Do not retrieve any document that contains this word." If the retrieval list retrieves too few documents, the OR operator can be used to increase recall; consider, for example, "encyclopedia" AND "online" OR "Internet" NOT "Encarta". This search will retrieve documents about online encyclopedias that use the term "Internet" instead of "online."
Phrase search. A phrase search matches only those documents that contain a specified phrase, such as "Wikipedia, the free encyclopedia."
Proximity search. A phrase search matches only those documents that contain two or more words that are separated by a specified number of words; a search for "Wikipedia" WITHIN2 "free" would retrieve only those documents in which the words "Wikipedia" and "free" occur within two words of each other.
Regular expression. A regular expression employs a complex but powerful querying syntax that can be used to specify retrieval conditions with precision.

[edit] Improved search algorithms

Technological advances have greatly improved the performance of free text searching. For example, Google's PageRank algorithm gives more prominence to documents to which other Web pages have linked. This algorithm dramatically improves users' perception of search precision, a fact that explains its popularity among Internet users. See search engine for additional examples.

[edit] Notes

↑1 In practice, it may be difficult to determine how a given search engine works. The search algorithms actually employed by Web search services are seldom fully disclosed out of fear that Web entrepreneurs will use search engine optimization techniques to improve their prominence in retrieval lists.