Full text search

July 19, 2012 § Leave a comment

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, the search engine examines all of the words in every stored document as it tries to match search criteria (e.g., words supplied by a user). Full text searching techniques became common in online bibliographic databases in the 1990s. Many 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.


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 rudimentary 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 its relative position within the document. Usually the indexer will ignore stop words, such as the English “the”, which are both too common and carry too little meaning to be useful for searching. Some indexers also employ language-specific stemming on the words being indexed, so for example any of the words “drives”, “drove”, or “driven” will be recorded in the index under a single concept word “drive”.

The precision vs. recall tradeoff

This diagram represents a low-precision, low-recall search as described in the text.

Recall measures the quantity of results returned by a search and precision is the measure of the quality of the results returned. Recall is the ratio of relevant results returned divided by all relevant results. Precision is the number of relevant results returned divided by the total number of results returned.

The diagram at right represents a low-precision, low-recall search. In the diagram the red and green dots represent the total population of potential search results for a given search. Red dots represent irrelevant results, and green dots represent relevant results. Relevancy is indicated by the proximity of search results to the center of the inner circle. Of all possible results shown, those that were actually returned by the search are shown on a light-blue background. In the example only one relevant result of three possible relevant results was returned, so the recall is a very low ratio of 1/3 or 33%. The precision for the example is a very low 1/4 or 25%, since only one of the four results returned was relevant.

Due to the ambiguities of natural language, full text search systems typically includes options like stop words to increase precision and stemming to increase recall. Controlled-vocabulary searching also helps alleviate low-precision issues by tagging documents in such a way that ambiguities are eliminated. The trade-off between precision and recall is simple: an increase in precision can lower overall recall while an increase in recall lowers precision.

False-positive problem

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. 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 “football”, clustering can be used to categorize the document/data universe into “American football”, “corporate football”, etc. Depending on the occurrences of words relevant to the categories, search terms a search result can be placed in one or more of the categories. This technique is being extensively deployed in the e-discovery domain.

Performance improvements

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.

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.” This increase in precision is very commonly counter-productive since it usually comes with a dramatic loss of recall.
  • Phrase search. A phrase search matches only those documents that contain a specified phrase, such as “Wikipedia, the free encyclopedia.”
  • Concept search. A search that is based on multi-word concepts, for example Compound term processing. This type of search is becoming popular in many e-Discovery solutions.
  • Concordance search. A concordance search produces an alphabetical list of all principal words that occur in a text with their immediate context.
  • 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.
  • Fuzzy search will search for document that match the given terms and some variation around them (using for instance edit distance to threshold the multiple variation)
  • Wildcard search. A search that substitutes one or more characters in a search query for a wildcard character such as an asterisk. For example using the asterisk in a search query “s*n” will find “sin”, “son”, “sun”, etc. in a text.

Improved search algorithms

The PageRank algorithm developed by Google gives more prominence to documents to which other Web pages have linked.


The following is a partial list of available software products whose predominant purpose is to perform full text indexing and searching. Some of these are accompanied with detailed descriptions of their theory of operation or internal algorithms, which can provide additional insight into how full text search may be accomplished.

Free and open source software

  • Apache Solr
  • BaseX
  • Clusterpoint Server (freeware licence for a single-server)
  • DataparkSearch
  • ElasticSearch [2] (Apache License, Version 2.0)
  • Ferret
  • ht://Dig
  • Hyper Estraier
  • KinoSearch
  • Lemur/Indri
  • Lucene
  • mnoGoSearch
  • Sphinx
  • Swish-e
  • Xapian

Proprietary software

  • askSam
  • Attivio
  • Autonomy Corporation
  • BA Insight
  • Brainware
  • BRS/Search
  • Clusterpoint Server (cluster license)
  • Concept Searching Limited
  • Dieselpoint
  • dtSearch
  • Endeca
  • Exalead
  • Fast Search & Transfer
  • Inktomi
  • Locayta
  • Lucid Imagination
  • MarkLogic
  • Vivísimo

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