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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 1 relevant result of 3 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 1 of the 4 results returned was relevant.
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837:"A method assigns importance ranks to nodes in a linked database, such as any database of documents containing citations, the world wide web or any other hypermedia database. The rank assigned to a document is calculated from the ranks of documents citing it. In addition, the rank of a document is..."
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Recall measures the quantity of relevant results returned by a search, while precision is the measure of the quality of the results returned. Recall is the ratio of relevant results returned to all relevant results. Precision is the ratio of the number of relevant results returned to the total number
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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
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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
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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
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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
334:. 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.
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The deficiencies of full 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.
396:. 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.
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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.
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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
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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
301:. In the sample diagram to the right, false positives are represented by the irrelevant results (red dots) that were returned by the search (on a light-blue background).
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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".
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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
218:(such as "the" and "and") that are both common and insufficiently meaningful to be useful in searching. Some indexers also employ language-specific
431:. 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
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or on parts of the original texts represented in databases (such as titles, abstracts, selected sections, or bibliographical references).
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operator says, in effect, "Do not retrieve any document that contains this word." If the retrieval list retrieves too few documents, the
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179:, employ full-text-search techniques, while others index only a portion of the web pages examined by their indexing systems.
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actually employed by web-search services are seldom fully disclosed out of fear that web entrepreneurs will use
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result can be placed in one or more of the categories. This technique is being extensively deployed in the
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will search for document that match the given terms and some variation around them (using for instance
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operator says, in effect, "Do not retrieve any document unless it contains both of these terms." The
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software) provide full-text-search capabilities. Some web search engines, such as the former
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421:. A concordance search produces an alphabetical list of all principal words that occur in a
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402:. A phrase search matches only those documents that contain a specified phrase, such as
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In practice, it may be difficult to determine how a given search engine works. The
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848:"SAP Adds HANA-Based Software Packages to IoT Portfolio | MarTech Advisor"
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805:. 12th International Conference on Data Engineering (ICDE'96). p. 164.
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Search and ranking algorithms for locating resources on the World Wide Web
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Full-text searching is likely to retrieve many documents that are not
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372:) can dramatically increase the precision of a full text search. The
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Experimental
Comparison of Schemes for Interpreting Boolean Queries
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that can be used to specify retrieval conditions with precision.
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445:. A regular expression employs a complex but powerful querying
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in the 1990s. Many websites and application programs (such as
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411:. A search that is based on multi-word concepts, for example
250:, full-text-search systems typically includes options like
152:. Full-text search is distinguished from searches based on
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techniques to improve their prominence in retrieval lists.
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would retrieve only those documents in which the words
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searching also helps alleviate low-precision issues by
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469:. For example, using the asterisk in a search query
60:. Unsourced material may be challenged and removed.
490:gives more prominence to documents to which other
473:will find "sin", "son", "sun", etc. in a text.
234:Diagram of a low-precision, low-recall search
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140:refers to techniques for searching a single
289:search question. Such documents are called
755:"Capabilities of Full Text Search System"
120:Learn how and when to remove this message
774:Pro Full-Text Search in SQL Server 2008
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167:from 1969, and became common in online
27:Search using the full text of documents
507:full-text search may be accomplished.
439:occur within two words of each other.
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459:to threshold the multiple variation)
195:". This is what some tools, such as
58:adding citations to reliable sources
870:cloud.google.com/enterprise-search
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384:operator can be used to increase
226:The precision vs. recall tradeoff
433:"Knowledge (XXG)" WITHIN2 "free"
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801:B., Yuwono; Lee, D. L. (1996).
304:Clustering techniques based on
45:needs additional citations for
348:, such as "Title" or "Author."
203:search terms (often called an
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512:Free and open source software
425:with their immediate context.
207:, but more correctly named a
948:Information retrieval genres
437:"Knowledge (XXG)" and "free"
390:"encyclopedia" AND "online"
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742:search engine optimization
702:Thunderstone Software LLC.
672:Fast Search & Transfer
478:Improved search algorithms
404:"Knowledge (XXG), the 💕."
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254:to increase precision and
246:Due to the ambiguities of
778:Apress Publishing Company
642:Concept Searching Limited
622:Bar Ilan Responsa Project
498:for additional examples.
388:; consider, for example,
159:In a full-text search, a
896:Compound term processing
413:Compound term processing
394:"Internet" NOT "Encarta"
360:operators (for example,
317:Performance improvements
772:Coles, Michael (2008).
486:algorithm developed by
338:Field-restricted search
326:Improved querying tools
169:bibliographic databases
927:Search engine indexing
906:Information extraction
852:www.martechadvisor.com
776:(Version 1 ed.).
277:False-positive problem
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911:Information retrieval
761:on December 23, 2010.
260:Controlled-vocabulary
239:of results returned.
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199:, do when searching.
191:, a strategy called "
148:or a collection in a
943:Text editor features
612:Autonomy Corporation
601:Proprietary software
356:. Searches that use
272:Precision and recall
258:to increase recall.
54:improve this article
588:Terrier IR Platform
923:, first FTS engine
866:"Vertex AI Search"
443:Regular expression
419:Concordance search
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150:full-text database
69:"Full-text search"
901:Enterprise search
787:978-1-4302-1594-3
738:search algorithms
682:Lucid Imagination
494:have linked. See
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16:(Redirected from
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528:ArangoSearch
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453:Fuzzy search
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52:Please help
47:verification
44:
647:Dieselpoint
553:mnoGoSearch
543:Lemur/Indri
523:Apache Solr
346:data record
311:e-discovery
209:concordance
110:August 2012
937:Categories
921:WebCrawler
828:US 6285999
724:References
637:BRS/Search
568:PostgreSQL
563:OpenSearch
538:KinoSearch
270:See also:
216:stop words
165:IBM STAIRS
80:newspapers
687:MarkLogic
632:Brainware
492:Web pages
370:"Encarta"
366:"online"
252:filtering
177:AltaVista
881:See also
717:Vivísimo
697:Swiftype
692:SAP HANA
652:dtSearch
502:Software
484:PageRank
467:asterisk
332:Keywords
313:domain.
306:Bayesian
287:intended
283:relevant
256:stemming
220:stemming
183:Indexing
154:metadata
146:document
144:-stored
142:computer
677:Inktomi
667:Exalead
607:Algolia
583:Swish-e
548:MariaDB
358:Boolean
285:to the
264:tagging
94:scholar
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662:Endeca
593:Xapian
578:Sphinx
488:Google
447:syntax
386:recall
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712:Vespa
558:MySQL
533:BaseX
471:"s*n"
342:field
293:(see
205:index
189:query
101:JSTOR
87:books
889:and
782:ISBN
482:The
423:text
197:grep
73:news
378:NOT
374:AND
368:NOT
364:AND
132:In
56:by
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