himanshuteotia · February 4, 2020 16:05
diff --git a/full-text-search.txt b/full-text-search.txt
 What is FULL-TEXT SEARCH? What does FULL-TEXT SEARCH mean? FULL-TEXT SEARCH meaning - FULL-TEXT SEARCH definition - FULL-TEXT SEARCH explanation.


 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 became common in online bibliographic databases in the 1990s. Many websites 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 their indexing systems.

 When dealing with a small number of documents, it is possible for the full-text-search engine to directly scan the contents of the documents with each query, a strategy called "serial scanning". This is what some tools, such as grep, do when searching.

 However, when the number of documents to search is potentially large, or the quantity of search queries to perform is substantial, the problem of full-text search is often divided into two tasks: indexing and searching. The indexing stage will scan the text of all the documents and build a list of search terms (often called an index, but more correctly named a concordance). In the search stage, when performing a specific query, only the index is referenced, rather than the text of the original documents.

 The indexer will make an entry in the index for each term or word found in a document, and possibly note its relative position within the document. Usually the indexer will ignore stop words (such as "the" and "and") that are both common and insufficiently meaningful to be useful in searching. Some indexers also employ language-specific stemming on the words being indexed. For example, the words "drives", "drove", and "driven" will be recorded in the index under the single concept word "drive".

 Free text searching is likely to retrieve many documents that are not relevant to the intended search question. Such documents are called false positives (see Type I error). The retrieval of irrelevant documents is often caused by the inherent ambiguity of natural language. In the sample diagram at right, false positives are represented by the irrelevant results (red dots) that were returned by the search (on a light-blue background).

 Clustering techniques based on Bayesian algorithms can help reduce false positives. For a search term of "bank", clustering can be used to categorize the document/data universe into "financial institution", "place to sit", "place to store" etc. Depending on the occurrences of words relevant to the categories, search terms or a search result can be placed in one or more of the categories. This technique is being extensively deployed in the e-discovery domain.

 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.
	What is FULL-TEXT SEARCH? What does FULL-TEXT SEARCH mean? FULL-TEXT SEARCH meaning - FULL-TEXT SEARCH definition - FULL-TEXT SEARCH explanation.


	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 became common in online bibliographic databases in the 1990s. Many websites 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 their indexing systems.

	When dealing with a small number of documents, it is possible for the full-text-search engine to directly scan the contents of the documents with each query, a strategy called "serial scanning". This is what some tools, such as grep, do when searching.

	However, when the number of documents to search is potentially large, or the quantity of search queries to perform is substantial, the problem of full-text search is often divided into two tasks: indexing and searching. The indexing stage will scan the text of all the documents and build a list of search terms (often called an index, but more correctly named a concordance). In the search stage, when performing a specific query, only the index is referenced, rather than the text of the original documents.

	The indexer will make an entry in the index for each term or word found in a document, and possibly note its relative position within the document. Usually the indexer will ignore stop words (such as "the" and "and") that are both common and insufficiently meaningful to be useful in searching. Some indexers also employ language-specific stemming on the words being indexed. For example, the words "drives", "drove", and "driven" will be recorded in the index under the single concept word "drive".

	Free text searching is likely to retrieve many documents that are not relevant to the intended search question. Such documents are called false positives (see Type I error). The retrieval of irrelevant documents is often caused by the inherent ambiguity of natural language. In the sample diagram at right, false positives are represented by the irrelevant results (red dots) that were returned by the search (on a light-blue background).

	Clustering techniques based on Bayesian algorithms can help reduce false positives. For a search term of "bank", clustering can be used to categorize the document/data universe into "financial institution", "place to sit", "place to store" etc. Depending on the occurrences of words relevant to the categories, search terms or a search result can be placed in one or more of the categories. This technique is being extensively deployed in the e-discovery domain.

	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.