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| {{Information science}}
| | Many individuals have this habit of doing all the stuff by themselves, irrespective of how important or simple they are! These individuals won't allow others interfere in their matters. While this stance could function inside other regions of life, it happens to be really not the best way to respond whenever you need to fix the Windows registry. There are some jobs including removing spywares, virus and also obsolete registry entries, that are best left to expert softwares. In this short article I usually tell we why it is important to fix Windows registry NOW!<br><br>Install an anti-virus software. If you absolutely have which on you computer then carry out a full program scan. If it finds any viruses on the computer, delete those. Viruses invade the computer plus make it slower. To protect the computer from numerous viruses, it is very greater to keep the anti-virus software running when you employ the web. We may moreover fix the protection settings of your web browser. It may block unknown and risky sites plus also block off any spyware or malware struggling to get into a computer.<br><br>StreamCI.dll is a file employed by the default Windows Audio driver to help process the various sound settings on the system. Although this file is one of the most crucial on countless different Windows systems, StreamCI.dll is continually causing a lot of errors that should be repaired. The advantageous news is the fact that you can fix this error by utilizing several effortless to perform steps which might resolve all the potential issues that are causing the error to show on the PC.<br><br>Always see with it that you have installed antivirus, anti-spyware plus anti-adware programs and have them up-to-date regularly. This can help stop windows XP running slow.<br><br>When it comes to software, this might be the vital piece since it is the 1 running a system and additional programs required inside the functions. Always maintain the cleanliness of your system from obsolete information by getting a good [http://bestregistrycleanerfix.com/registry-reviver registry reviver]. Protect it from a virus online by providing a workable virus security program. You should have a monthly clean up by running a defragmenter program. This way it might enhance the performance of the computer and for we to avoid any mistakes. If you think something is wrong with the computer software, plus we don't recognize how to fix it then refer to a technician.<br><br>Turn It Off: Chances are in the event you are like me; then we spend a lot of time on a computer on a daily basis. Try offering your computer certain time to do completely nothing; this can sound funny nevertheless should you have an older computer you are asking it to do too much.<br><br>As the hub center of the computer, all of the important settings are stored the registry. Registry is structured as keys and every key relates to a system. The system reads the keys and utilizes the data to launch and run programs. However, the big problem is that there are too many unwanted settings, useless information occuping the valuable space. It makes the program run gradually and huge amounts of settings become unreadable.<br><br>If you like to have a computer with quick running speed, you'd better install a advantageous registry cleaner to wash the useless files for you. As long as you take care of the computer, it usually keep inside advantageous condition. |
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| '''Information retrieval''' is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on metadata or on full-text (or other content-based) indexing.
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| Automated information retrieval systems are used to reduce what has been called "[[information overload]]". Many universities and [[public library|public libraries]] use IR systems to provide access to books, journals and other documents. [[Web search engine]]s are the most visible [[Information retrieval applications|IR applications]].
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| == Overview ==
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| An information retrieval process begins when a user enters a [[query string|query]] into the system. Queries are formal statements of [[information need]]s, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of [[relevance|relevancy]].
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| An object is an entity that is represented by information in a [[database]]. User queries are matched against the database information. Depending on the [[Information retrieval applications|application]] the data objects may be, for example, text documents, images,<ref name=goodron2000>{{cite journal |first=Abby A. |last=Goodrum |title=Image Information Retrieval: An Overview of Current Research |journal=Informing Science |volume=3 |number=2 |year=2000 }}</ref> audio,<ref name=Foote99>{{cite journal |first=Jonathan |last=Foote |title=An overview of audio information retrieval |journal=Multimedia Systems |year=1999 |publisher=Springer }}</ref> [[mind maps]]<ref name=Beel2009>{{cite journal |first=Jöran |last=Beel |first2=Bela |last2=Gipp |first3=Jan-Olaf |last3=Stiller |contribution=Information Retrieval On Mind Maps - What Could It Be Good For? |contribution-url=http://www.sciplore.org/publications_en.php |title=Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom'09) |year=2009 |publisher=IEEE |place=Washington, DC }}</ref> or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata.
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| Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.<ref name="Frakes1992">{{cite book |last=Frakes |first=William B. |title=Information Retrieval Data Structures & Algorithms |publisher=Prentice-Hall, Inc. |year=1992 |isbn=0-13-463837-9 |url=http://www.scribd.com/doc/13742235/Information-Retrieval-Data-Structures-Algorithms-William-B-Frakes }}</ref>
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| == History ==
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| {{Rquote|right|But do you know that, although I have kept the diary [on a phonograph] for months past, it never once struck me how I was going to find any particular part of it in case I wanted to look it up?|[[John Seward|Dr Seward]]| [[Bram Stoker]]'s ''[[Dracula]]'',
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| 1897}}
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| The idea of using computers to search for relevant pieces of information was popularized in the article ''[[As We May Think]]'' by [[Vannevar Bush]] in 1945.<ref name="Singhal2001">{{cite journal |last=Singhal |first=Amit |title=Modern Information Retrieval: A Brief Overview |journal=Bulletin of the IEEE Computer Society Technical Committee on Data Engineering |volume=24 |issue=4 |pages=35–43 |year =2001 |url=http://singhal.info/ieee2001.pdf }}</ref> The first automated information retrieval systems were introduced in the 1950s and 1960s. By 1970 several different techniques had been shown to perform well on small [[text corpora]] such as the Cranfield collection (several thousand documents).<ref name="Singhal2001" /> Large-scale retrieval systems, such as the Lockheed Dialog system, came into use early in the 1970s.
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| In 1992, the US Department of Defense along with the [[National Institute of Standards and Technology]] (NIST), cosponsored the [[Text Retrieval Conference]] (TREC) as part of the TIPSTER text program. The aim of this was to look into the information retrieval community by supplying the infrastructure that was needed for evaluation of text retrieval methodologies on a very large text collection. This catalyzed research on methods that [[scalability|scale]] to huge corpora. The introduction of web [[Web search engine|search engine]]s has boosted the need for very large scale retrieval systems even further.
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| == Model types ==
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| [[File:Information-Retrieval-Models.png|thumb|500px|Categorization of IR-models (translated from [[:de:Informationsrückgewinnung#Klassifikation von Modellen zur Repräsentation natürlichsprachlicher Dokumente|German entry]], original source [http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=0514&lng=eng&id= Dominik Kuropka]).]]
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| For effectively retrieving relevant documents by IR strategies, the documents are typically transformed into a suitable representation. Each retrieval strategy incorporate a specific model for its document representation purposes. The picture on the right illustrates the relationship of some common models. In the picture, the models are categorized according to two dimensions: the mathematical basis and the properties of the model.
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| === First dimension: mathematical basis ===
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| * ''Set-theoretic'' models represent documents as sets of words or phrases. Similarities are usually derived from set-theoretic operations on those sets. Common models are:
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| ** [[Standard Boolean model]]
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| ** [[Extended Boolean model]]
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| ** [[Fuzzy retrieval]]
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| * ''Algebraic models'' represent documents and queries usually as vectors, matrices, or tuples. The similarity of the query vector and document vector is represented as a scalar value.
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| ** [[Vector space model]]
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| ** [[Generalized vector space model]]
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| ** [[Topic-based vector space model|(Enhanced) Topic-based Vector Space Model]]
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| ** [[Extended Boolean model]]
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| ** [[Latent semantic indexing]] aka [[latent semantic analysis]]
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| * ''Probabilistic models'' treat the process of document retrieval as a probabilistic inference. Similarities are computed as probabilities that a document is relevant for a given query. Probabilistic theorems like the [[Bayes' theorem]] are often used in these models.
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| ** [[Binary Independence Model]]
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| ** [[Probabilistic relevance model]] on which is based the [[Probabilistic relevance model (BM25)|okapi (BM25)]] relevance function
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| ** [[Uncertain inference]]
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| ** [[Language model]]s
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| ** [[Divergence-from-randomness model]]
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| ** [[Latent Dirichlet allocation]]
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| * ''Feature-based retrieval models'' view documents as vectors of values of ''feature functions'' (or just ''features'') and seek the best way to combine these features into a single relevance score, typically by [[learning to rank]] methods. Feature functions are arbitrary functions of document and query, and as such can easily incorporate almost any other retrieval model as just a yet another feature.
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| === Second dimension: properties of the model ===
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| * ''Models without term-interdependencies'' treat different terms/words as independent. This fact is usually represented in vector space models by the [[orthogonality]] assumption of term vectors or in probabilistic models by an [[Independence (mathematical logic)|independency]] assumption for term variables.
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| * ''Models with immanent term interdependencies'' allow a representation of interdependencies between terms. However the degree of the interdependency between two terms is defined by the model itself. It is usually directly or indirectly derived (e.g. by [[dimension reduction|dimensional reduction]]) from the [[co-occurrence]] of those terms in the whole set of documents.
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| * ''Models with transcendent term interdependencies'' allow a representation of interdependencies between terms, but they do not allege how the interdependency between two terms is defined. They relay an external source for the degree of interdependency between two terms. (For example a human or sophisticated algorithms.)
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| == Performance and correctness measures ==
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| {{main|Precision and recall}}
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| Many different measures for evaluating the performance of information retrieval systems have been proposed. The measures require a collection of documents and a query. All common measures described here assume a ground truth notion of relevancy: every document is known to be either relevant or non-relevant to a particular query. In practice queries may be [[ill-posed]] and there may be different shades of relevancy.
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| === Precision ===
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| Precision is the fraction of the documents retrieved that are [[Relevance (information retrieval)|relevant]] to the user's information need.
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| :<math> \mbox{precision}=\frac{|\{\mbox{relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{retrieved documents}\}|} </math>
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| In [[binary classification]], precision is analogous to [[positive predictive value]]. Precision takes all retrieved documents into account. It can also be evaluated at a given cut-off rank, considering only the topmost results returned by the system. This measure is called ''precision at n'' or ''P@n''.
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| Note that the meaning and usage of "precision" in the field of Information Retrieval differs from the definition of [[accuracy and precision]] within other branches of science and [[statistics]].
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| === Recall ===
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| Recall is the fraction of the documents that are relevant to the query that are successfully retrieved.
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| :<math>\mbox{recall}=\frac{|\{\mbox{relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{relevant documents}\}|} </math>
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| In binary classification, recall is often called [[sensitivity and specificity|sensitivity]]. So it can be looked at as ''the probability that a relevant document is retrieved by the query''.
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| It is trivial to achieve recall of 100% by returning all documents in response to any query. Therefore recall alone is not enough but one needs to measure the number of non-relevant documents also, for example by computing the precision.
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| === Fall-out ===
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| The proportion of non-relevant documents that are retrieved, out of all non-relevant documents available:
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| :<math> \mbox{fall-out}=\frac{|\{\mbox{non-relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{non-relevant documents}\}|} </math>
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| In binary classification, fall-out is closely related to [[sensitivity and specificity|specificity]] and is equal to <math>(1-\mbox{specificity})</math>. It can be looked at as ''the probability that a non-relevant document is retrieved by the query''.
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| It is trivial to achieve fall-out of 0% by returning zero documents in response to any query.
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| === F-measure ===
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| {{main|F-score}}
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| The weighted [[harmonic mean]] of precision and recall, the traditional F-measure or balanced F-score is:
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| :<math>F = \frac{2 \cdot \mathrm{precision} \cdot \mathrm{recall}}{(\mathrm{precision} + \mathrm{recall})}.\,</math>
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| This is also known as the <math>F_1</math> measure, because recall and precision are evenly weighted.
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| The general formula for non-negative real <math>\beta</math> is:
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| :<math>F_\beta = \frac{(1 + \beta^2) \cdot (\mathrm{precision} \cdot \mathrm{recall})}{(\beta^2 \cdot \mathrm{precision} + \mathrm{recall})}\,</math>.
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| Two other commonly used F measures are the <math>F_{2}</math> measure, which weights recall twice as much as precision, and the <math>F_{0.5}</math> measure, which weights precision twice as much as recall.
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| The F-measure was derived by van Rijsbergen (1979) so that <math>F_\beta</math> "measures the effectiveness of retrieval with respect to a user who attaches <math>\beta</math> times as much importance to recall as precision". It is based on van Rijsbergen's effectiveness measure <math>E = 1 - \frac{1}{\frac{\alpha}{P} + \frac{1-\alpha}{R}}</math>. Their relationship is <math>F_\beta = 1 - E</math> where <math>\alpha=\frac{1}{1 + \beta^2}</math>.
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| === Average precision ===
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| <!-- [[Average precision]] redirects here -->
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| Precision and recall are single-value metrics based on the whole list of documents returned by the system. For systems that return a ranked sequence of documents, it is desirable to also consider the order in which the returned documents are presented. By computing a precision and recall at every position in the ranked sequence of documents, one can plot a precision-recall curve, plotting precision <math>p(r)</math> as a function of recall <math>r</math>. Average precision computes the average value of <math>p(r)</math> over the interval from <math>r=0</math> to <math>r=1</math>:<ref name="zhu2004">{{cite journal |first=Mu |last=Zhu |contribution=Recall, Precision and Average Precision |contribution-url=http://sas.uwaterloo.ca/stats_navigation/techreports/04WorkingPapers/2004-09.pdf |year=2004 }}</ref>
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| :<math>\operatorname{AveP} = \int_0^1 p(r)dr</math>
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| That is the area under the precision-recall curve.
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| This integral is in practice replaced with a finite sum over every position in the ranked sequence of documents:
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| :<math>\operatorname{AveP} = \sum_{k=1}^n P(k) \Delta r(k)</math>
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| where <math>k</math> is the rank in the sequence of retrieved documents, <math>n</math> is the number of retrieved documents, <math>P(k)</math> is the precision at cut-off <math>k</math> in the list, and <math>\Delta r(k)</math> is the change in recall from items <math>k-1</math> to <math>k</math>.<ref name="zhu2004" />
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| This finite sum is equivalent to:
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| :<math> \operatorname{AveP} = \frac{\sum_{k=1}^n (P(k) \times \operatorname{rel}(k))}{\mbox{number of relevant documents}} \!</math>
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| where <math>\operatorname{rel}(k)</math> is an indicator function equaling 1 if the item at rank <math>k</math> is a relevant document, zero otherwise.<ref name="Turpin2006">{{cite journal |last=Turpin |first=Andrew |last2=Scholer |first2=Falk |title=User performance versus precision measures for simple search tasks |journal=Proceedings of the 29th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Seattle, WA, August 06–11, 2006) |publisher=ACM |location=New York, NY |pages=11–18 |doi=10.1145/1148170.1148176 |year=2006 |isbn=1-59593-369-7 }}</ref> Note that the average is over all relevant documents and the relevant documents not retrieved get a precision score of zero.
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| Some authors choose to interpolate the <math>p(r)</math> function to reduce the impact of "wiggles" in the curve.<ref name=voc2010>{{cite journal |last=Everingham |first=Mark |last2=Van Gool |first2=Luc |last3=Williams |first3=Christopher K. I. |last4=Winn |first4=John |last5=Zisserman |first5=Andrew |title=The PASCAL Visual Object Classes (VOC) Challenge |journal=International Journal of Computer Vision |volume=88 |issue=2 |pages=303–338 |publisher=Springer |date=June 2010 |url=http://pascallin.ecs.soton.ac.uk/challenges/VOC/pubs/everingham10.pdf |accessdate=2011-08-29 |doi=10.1007/s11263-009-0275-4 }}</ref><ref name="nlpbook">{{cite book |last=Manning |first=Christopher D. |last2=Raghavan |first2=Prabhakar |last3=Schütze |first3=Hinrich |title=Introduction to Information Retrieval |publisher=Cambridge University Press |year=2008 |url=http://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-ranked-retrieval-results-1.html }}</ref> For example, the PASCAL Visual Object Classes challenge (a benchmark for computer vision object detection) computes average precision by averaging the precision over a set of evenly spaced recall levels {0, 0.1, 0.2, ... 1.0}:<ref name="voc2010" /><ref name="nlpbook" />
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| :<math>\operatorname{AveP} = \frac{1}{11} \sum_{r \in \{0, 0.1, \ldots, 1.0\}} p_{\operatorname{interp}}(r)</math>
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| where <math>p_{\operatorname{interp}}(r)</math> is an interpolated precision that takes the maximum precision over all recalls greater than <math>r</math>:
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| :<math>p_{\operatorname{interp}}(r) = \operatorname{max}_{\tilde{r}:\tilde{r} \geq r} p(\tilde{r})</math>.
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| An alternative is to derive an analytical <math>p(r)</math> function by assuming a particular parametric distribution for the underlying decision values. For example, a ''binormal precision-recall curve'' can be obtained by assuming decision values in both classes to follow a Gaussian distribution.<ref>K.H. Brodersen, C.S. Ong, K.E. Stephan, J.M. Buhmann (2010). [http://icpr2010.org/pdfs/icpr2010_ThBCT8.28.pdf The binormal assumption on precision-recall curves]. ''Proceedings of the 20th International Conference on Pattern Recognition'', 4263-4266.</ref>
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| === R-Precision ===
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| Precision at '''R'''-th position in the ranking of results for a query that has '''R''' relevant documents. This measure is highly correlated to Average Precision. Also, Precision is equal to Recall at the '''R'''-th position.
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| === Mean average precision ===
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| <!-- [[Mean average precision]] redirects here -->
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| Mean average precision for a set of queries is the mean of the average precision scores for each query.
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| :<math> \operatorname{MAP} = \frac{\sum_{q=1}^Q \operatorname{AveP(q)}}{Q} \!</math>
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| where ''Q'' is the number of queries.
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| === Discounted cumulative gain ===
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| {{main|Discounted cumulative gain}}
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| DCG uses a graded relevance scale of documents from the result set to evaluate the usefulness, or gain, of a document based on its position in the result list. The premise of DCG is that highly relevant documents appearing lower in a search result list should be penalized as the graded relevance value is reduced logarithmically proportional to the position of the result.
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| The DCG accumulated at a particular rank position <math>p</math> is defined as:
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| :<math> \mathrm{DCG_{p}} = rel_{1} + \sum_{i=2}^{p} \frac{rel_{i}}{\log_{2}i}. </math>
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| Since result set may vary in size among different queries or systems, to compare performances the normalised version of DCG uses an ideal DCG. To this end, it sorts documents of a result list by relevance, producing an ideal DCG at position p (<math>IDCG_p</math>), which normalizes the score:
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| :<math> \mathrm{nDCG_{p}} = \frac{DCG_{p}}{IDCG{p}}. </math>
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| The nDCG values for all queries can be averaged to obtain a measure of the average performance of a ranking algorithm. Note that in a perfect ranking algorithm, the <math>DCG_p</math> will be the same as the <math>IDCG_p</math> producing an nDCG of 1.0. All nDCG calculations are then relative values on the interval 0.0 to 1.0 and so are cross-query comparable.
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| === Other Measures ===
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| * [[Mean reciprocal rank]]
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| * [[Spearman's rank correlation coefficient]]
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| === Timeline ===
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| * Before the '''1900s'''
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| *: '''1801''': [[Joseph Marie Jacquard]] invents the [[Jacquard loom]], the first machine to use punched cards to control a sequence of operations.
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| *: '''1880s''': [[Herman Hollerith]] invents an electro-mechanical data tabulator using punch cards as a machine readable medium.
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| *: '''1890''' Hollerith [[Punched cards|cards]], [[keypunch]]es and [[Tabulating machine|tabulators]] used to process the [[1890 US Census]] data.
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| * '''1920s-1930s'''
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| *: [[Emanuel Goldberg]] submits patents for his "Statistical Machine” a document search engine that used photoelectric cells and pattern recognition to search the metadata on rolls of microfilmed documents.
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| * '''1940s–1950s'''
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| *: '''late 1940s''': The US military confronted problems of indexing and retrieval of wartime scientific research documents captured from Germans.
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| *:: '''1945''': [[Vannevar Bush]]'s ''[[As We May Think]]'' appeared in ''[[Atlantic Monthly]]''.
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| *:: '''1947''': [[Hans Peter Luhn]] (research engineer at IBM since 1941) began work on a mechanized punch card-based system for searching chemical compounds.
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| *: '''1950s''': Growing concern in the US for a "science gap" with the USSR motivated, encouraged funding and provided a backdrop for mechanized literature searching systems (Allen Kent ''et al.'') and the invention of citation indexing ([[Eugene Garfield]]).
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| *: '''1950''': The term "information retrieval" appears to have been coined by [[Calvin Mooers]].<ref>Mooers, Calvin N.; ''Theory Digital Handling Non-numerical Information'' (Zator Technical Bulletin No. 48) 5, cited in "information, n.". OED Online. December 2011. Oxford University Press.</ref>
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| *: '''1951''': Philip Bagley conducted the earliest experiment in computerized document retrieval in a master thesis at [[MIT]].<ref name="Doyle1975">{{cite book |last=Doyle |first=Lauren |last2=Becker |first2=Joseph |title=Information Retrieval and Processing |publisher=Melville |year=1975 |pages=410 pp. |isbn=0-471-22151-1 }}</ref>
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| *: '''1955''': Allen Kent joined [[Case Western Reserve University]], and eventually became associate director of the Center for Documentation and Communications Research. That same year, Kent and colleagues published a paper in American Documentation describing the precision and recall measures as well as detailing a proposed "framework" for evaluating an IR system which included statistical sampling methods for determining the number of relevant documents not retrieved.
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| *: '''1958''': International Conference on Scientific Information Washington DC included consideration of IR systems as a solution to problems identified. See: ''Proceedings of the International Conference on Scientific Information, 1958'' (National Academy of Sciences, Washington, DC, 1959)
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| *: '''1959''': [[Hans Peter Luhn]] published "Auto-encoding of documents for information retrieval."
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| * '''1960s''':
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| *: '''early 1960s''': [[Gerard Salton]] began work on IR at Harvard, later moved to Cornell.
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| *: '''1960''': [[Melvin Earl Maron]] and John Lary<!-- sic --> Kuhns<ref name="Maron2008">{{cite journal |title=An Historical Note on the Origins of Probabilistic Indexing |last=Maron | first=Melvin E. |journal=Information Processing and Management |volume=44 |year=2008 |pages=971–972 |url=http://yunus.hacettepe.edu.tr/~tonta/courses/spring2008/bby703/maron-on-probabilistic%20indexing-2008.pdf |doi=10.1016/j.ipm.2007.02.012 |issue=2 }}</ref> published "On relevance, probabilistic indexing, and information retrieval" in the Journal of the ACM 7(3):216–244, July 1960.
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| *: '''1962''':
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| *:* [[Cyril W. Cleverdon]] published early findings of the Cranfield studies, developing a model for IR system evaluation. See: Cyril W. Cleverdon, "Report on the Testing and Analysis of an Investigation into the Comparative Efficiency of Indexing Systems". Cranfield Collection of Aeronautics, Cranfield, England, 1962.
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| *:* Kent published ''Information Analysis and Retrieval''.
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| *: '''1963''':
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| *:* Weinberg report "Science, Government and Information" gave a full articulation of the idea of a "crisis of scientific information." The report was named after Dr. [[Alvin Weinberg]].
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| *:* Joseph Becker and [[Robert M. Hayes]] published text on information retrieval. Becker, Joseph; Hayes, Robert Mayo. ''Information storage and retrieval: tools, elements, theories''. New York, Wiley (1963).
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| *: '''1964''':
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| *:* [[Karen Spärck Jones]] finished her thesis at Cambridge, ''Synonymy and Semantic Classification'', and continued work on [[computational linguistics]] as it applies to IR.
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| *:* The [[National Bureau of Standards]] sponsored a symposium titled "Statistical Association Methods for Mechanized Documentation." Several highly significant papers, including G. Salton's first published reference (we believe) to the [[SMART Information Retrieval System|SMART]] system.
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| *:'''mid-1960s''':
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| *::* National Library of Medicine developed [[MEDLARS]] Medical Literature Analysis and Retrieval System, the first major machine-readable database and batch-retrieval system.
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| *::* Project Intrex at MIT.
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| *:: '''1965''': [[J. C. R. Licklider]] published ''Libraries of the Future''.
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| *:: '''1966''': [[Don Swanson]] was involved in studies at University of Chicago on Requirements for Future Catalogs.
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| *: '''late 1960s''': [[F. Wilfrid Lancaster]] completed evaluation studies of the MEDLARS system and published the first edition of his text on information retrieval.
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| *:: '''1968''':
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| *:* Gerard Salton published ''Automatic Information Organization and Retrieval''.
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| *:* John W. Sammon, Jr.'s RADC Tech report "Some Mathematics of Information Storage and Retrieval..." outlined the vector model.
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| *:: '''1969''': Sammon's "A nonlinear mapping for data structure analysis" (IEEE Transactions on Computers) was the first proposal for visualization interface to an IR system.
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| * '''1970s'''
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| *: '''early 1970s''':
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| *::* First online systems—NLM's AIM-TWX, MEDLINE; Lockheed's Dialog; SDC's ORBIT.
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| *::* [[Theodor Nelson]] promoting concept of [[hypertext]], published ''Computer Lib/Dream Machines''.
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| *: '''1971''': [[Nicholas Jardine]] and [[Cornelis J. van Rijsbergen]] published "The use of hierarchic clustering in information retrieval", which articulated the "cluster hypothesis."<ref>{{cite journal|author=N. Jardine, C.J. van Rijsbergen|title=The use of hierarchic clustering in information retrieval|journal=Information Storage and Retrieval|volume=7|issue=5|pages=217–240|date=December 1971|doi=10.1016/0020-0271(71)90051-9}}</ref>
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| *: '''1975''': Three highly influential publications by Salton fully articulated his vector processing framework and term discrimination model:
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| *::* ''A Theory of Indexing'' (Society for Industrial and Applied Mathematics)
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| *::* ''A Theory of Term Importance in Automatic Text Analysis'' ([[JASIS]] v. 26)
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| *::* ''A Vector Space Model for Automatic Indexing'' ([[Communications of the ACM|CACM]] 18:11)
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| *: '''1978''': The First [[Association for Computing Machinery|ACM]] [[Special Interest Group on Information Retrieval|SIGIR]] conference.
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| *: '''1979''': C. J. van Rijsbergen published ''Information Retrieval'' (Butterworths). Heavy emphasis on probabilistic models.
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| * '''1980s'''
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| *: '''1980''': First international ACM SIGIR conference, joint with British Computer Society IR group in Cambridge.
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| *: '''1982''': [[Nicholas J. Belkin]], Robert N. Oddy, and Helen M. Brooks proposed the ASK (Anomalous State of Knowledge) viewpoint for information retrieval. This was an important concept, though their automated analysis tool proved ultimately disappointing.
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| *: '''1983''': Salton (and Michael J. McGill) published ''Introduction to Modern Information Retrieval'' (McGraw-Hill), with heavy emphasis on vector models.
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| *: '''1985''': David Blair and [[Bill Maron]] publish: An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System
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| *: '''mid-1980s''': Efforts to develop end-user versions of commercial IR systems.
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| *:: '''1985–1993''': Key papers on and experimental systems for visualization interfaces.
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| *:: Work by [[Donald B. Crouch]], [[Robert R. Korfhage]], Matthew Chalmers, Anselm Spoerri and others.
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| *: '''1989''': First [[World Wide Web]] proposals by [[Tim Berners-Lee]] at [[CERN]].
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| * '''1990s'''
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| *: '''1992''': First [[Text Retrieval Conference|TREC]] conference.
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| *: '''1997''': Publication of [[Robert R. Korfhage|Korfhage]]'s ''Information Storage and Retrieval''<ref name="Korfhage1997">{{cite book |last=Korfhage |first=Robert R. |title=Information Storage and Retrieval |publisher=Wiley |year=1997 |pages=368 pp. |isbn=978-0-471-14338-3 |url=http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471143383,descCd-authorInfo.html }}</ref> with emphasis on visualization and multi-reference point systems.
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| *: '''late 1990s''': Web [[Web search engine|search engines]] implementation of many features formerly found only in experimental IR systems. Search engines become the most common and maybe best instantiation of IR models, research, and implement by Jocanz.
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| == Awards in the field ==
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| * [[Tony Kent Strix award]]
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| * [[Gerard Salton Award]]
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| ==See also==
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| {{col-begin}}
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| {{col-break}}
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| * [[Adversarial information retrieval]]
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| * [[Collaborative information seeking]]
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| * [[Controlled vocabulary]]
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| * [[Cross-language information retrieval]]
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| * [[Data mining]]
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| * [[European Summer School in Information Retrieval]]
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| * [[Human–computer information retrieval]]
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| * [[Information extraction]]
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| * [[Information Retrieval Facility]]
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| * [[Knowledge visualization]]
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| * [[Multimedia Information Retrieval]]
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| * [[List of information retrieval libraries]]
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| {{col-break}}
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| * [[Personal information management]]
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| * [[Relevance (Information Retrieval)]]
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| * [[Relevance feedback]]
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| * [[Rocchio Classification]]
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| * [[Index (search engine)|Search index]]
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| * [[Social information seeking]]
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| * [[Special Interest Group on Information Retrieval]]
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| * [[Structured Search]]
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| * [[Subject indexing]]
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| * [[Temporal information retrieval]]
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| * [[Tf-idf]]
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| * [[XML-Retrieval]]
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| * [[Key-objects]]
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| {{col-end}}
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| == References ==
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| {{reflist}}
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| ==External links==
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| {{wikiquote}}
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| * [http://www.acm.org/sigir/ ACM SIGIR: Information Retrieval Special Interest Group]
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| * [http://irsg.bcs.org/ BCS IRSG: British Computer Society - Information Retrieval Specialist Group]
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| * [http://trec.nist.gov Text Retrieval Conference (TREC)]
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| * [http://www.isical.ac.in/~fire Forum for Information Retrieval Evaluation (FIRE)]
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| * [http://www.dcs.gla.ac.uk/Keith/Preface.html Information Retrieval] (online book) by [[C. J. van Rijsbergen]]
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| * [http://ir.dcs.gla.ac.uk/wiki/ Information Retrieval Wiki]
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| * [http://ir-facility.org/ Information Retrieval Facility]
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| * [http://www.nonrelevant.net Information Retrieval @ DUTH]
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| * [http://nlp.stanford.edu/IR-book/ Introduction to Information Retrieval (online book) by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press. 2008. ]
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| {{DEFAULTSORT:Information Retrieval}}
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| [[Category:Articles with inconsistent citation formats]]
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| [[Category:Information retrieval| ]]
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| [[Category:Natural language processing]]
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