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Information retrieval

information retrieval, information retrieval services
Information retrieval IR is the activity of obtaining information resources relevant to an information need from a collection of information resources Searches can be based on full-text or other content-based indexing

Automated information retrieval systems are used to reduce what has been called "information overload" Many universities and public libraries use IR systems to provide access to books, journals and other documents Web search engines are the most visible IR applications


  • 1 Overview
  • 2 History
  • 3 Model types
    • 31 First dimension: mathematical basis
    • 32 Second dimension: properties of the model
  • 4 Performance and correctness measures
    • 41 Precision
    • 42 Recall
    • 43 Fall-out
    • 44 F-score / F-measure
    • 45 Average precision
    • 46 Precision at K
    • 47 R-Precision
    • 48 Mean average precision
    • 49 Discounted cumulative gain
    • 410 Other measures
    • 411 Visualization
  • 5 Timeline
  • 6 Awards in the field
  • 7 Leading IR Research Groups
  • 8 See also
  • 9 References
  • 10 Further reading
  • 11 External links


An information retrieval process begins when a user enters a query into the system Queries are formal statements of information needs, 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 relevancy

An object is an entity that is represented by information in a content collection or database User queries are matched against the database information However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked This ranking of results is a key difference of information retrieval searching compared to database searching

Depending on the application the data objects may be, for example, text documents, images, audio, mind maps 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

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


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 It would appear that Bush was inspired by patents for a 'statistical machine' - filed by Emanuel Goldberg in the 1920s and '30s - that searched for documents stored on film The first description of a computer searching for information was described by Holmstrom in 1948, detailing an early mention of the Univac computer Automated information retrieval systems were introduced in the 1950s: one even featured in the 1957 romantic comedy, Desk Set In the 1960s, the first large information retrieval research group was formed by Gerard Salton at Cornell By the 1970s several different retrieval techniques had been shown to perform well on small text corpora such as the Cranfield collection several thousand documents Large-scale retrieval systems, such as the Lockheed Dialog system, came into use early in the 1970s

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 scale to huge corpora The introduction of web search engines has boosted the need for very large scale retrieval systems even further

Model types

Categorization of IR-models translated from German entry, original source Dominik Kuropka

For effectively retrieving relevant documents by IR strategies, the documents are typically transformed into a suitable representation Each retrieval strategy incorporates 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

First dimension: mathematical basis

  • 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:
    • Standard Boolean model
    • Extended Boolean model
    • Fuzzy retrieval
  • 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
    • Vector space model
    • Generalized vector space model
    • Enhanced Topic-based Vector Space Model
    • Extended Boolean model
    • Latent semantic indexing aka latent semantic analysis
  • 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
    • Binary Independence Model
    • Probabilistic relevance model on which is based the okapi BM25 relevance function
    • Uncertain inference
    • Language models
    • Divergence-from-randomness model
    • Latent Dirichlet allocation
  • 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 another feature

Second dimension: properties of the model

  • 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 independency assumption for term variables
  • 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 eg by dimensional reduction from the co-occurrence of those terms in the whole set of documents
  • 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 rely an external source for the degree of interdependency between two terms For example, a human or sophisticated algorithms

Performance and correctness measures

Further information: Evaluation measures information retrieval

The evaluation of an information retrieval system is the process of assessing how well a system meets the information needs of its users Traditional evaluation metrics, designed for Boolean retrieval or top-k retrieval, include precision and recall Many more measures for evaluating the performance of information retrieval systems have also been proposed In general, measurement considers a collection of documents to be searched and a search 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

Virtually all modern evaluation metrics eg, mean average precision, discounted cumulative gain are designed for ranked retrieval without any explicit rank cutoff, taking into account the relative order of the documents retrieved by the search engines and giving more weight to documents returned at higher ranks

The mathematical symbols used in the formulas below mean:

  • X ∩ Y - Intersection - in this case, specifying the documents in both sets X and Y
  • | X | - Cardinality - in this case, the number of documents in set X
  • ∫ - Integral
  • ∑ - Summation
  • Δ - Symmetric difference


Main article: Precision and recall

Precision is the fraction of the documents retrieved that are relevant to the user's information need

precision = | ∩ | | | =\\cap \\|\|

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

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


Main article: Precision and recall

Recall is the fraction of the documents that are relevant to the query that are successfully retrieved

recall = | ∩ | | | =\\cap \\|\|

In binary classification, recall is often called sensitivity So it can be looked at as the probability that a relevant document is retrieved by the query

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


The proportion of non-relevant documents that are retrieved, out of all non-relevant documents available:

fall-out = | ∩ | | | =\\cap \\|\|

In binary classification, fall-out is closely related to specificity and is equal to 1 − specificity It can be looked at as the probability that a non-relevant document is retrieved by the query

It is trivial to achieve fall-out of 0% by returning zero documents in response to any query

F-score / F-measure

Main article: F-score

The weighted harmonic mean of precision and recall, the traditional F-measure or balanced F-score is:

F = 2 ⋅ p r e c i s i o n ⋅ r e c a l l p r e c i s i o n + r e c a l l \cdot \mathrm +\mathrm

This is also known as the F 1 measure, because recall and precision are evenly weighted

The general formula for non-negative real β is:

F β = 1 + β 2 ⋅ p r e c i s i o n ⋅ r e c a l l β 2 ⋅ p r e c i s i o n + r e c a l l =\cdot \mathrm \cdot \mathrm \cdot \mathrm +\mathrm \,

Two other commonly used F measures are the F 2 measure, which weights recall twice as much as precision, and the F 05 measure, which weights precision twice as much as recall

The F-measure was derived by van Rijsbergen 1979 so that F β "measures the effectiveness of retrieval with respect to a user who attaches β times as much importance to recall as precision" It is based on van Rijsbergen's effectiveness measure E = 1 − 1 α P + 1 − α R + Their relationship is:

F β = 1 − E =1-E where α = 1 1 + β 2

F-measure can be a better single metric when compared to precision and recall; both precision and recall give different information that can complement each other when combined If one of them excels more than the other, F-measure will reflect it

Average precision

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 p r as a function of recall r Average precision computes the average value of p r over the interval from r = 0 to r = 1 :

AveP = ∫ 0 1 p r d r =\int _^prdr

That is the area under the precision-recall curve This integral is in practice replaced with a finite sum over every position in the ranked sequence of documents:

AveP = ∑ k = 1 n P k Δ r k =\sum _^Pk\Delta rk

where k is the rank in the sequence of retrieved documents, n is the number of retrieved documents, P k is the precision at cut-off k in the list, and Δ r k is the change in recall from items k − 1 to k

This finite sum is equivalent to:

AveP = ∑ k = 1 n P k × rel ⁡ k number of relevant documents =^Pk\times \operatorname k\!

where rel ⁡ k k is an indicator function equaling 1 if the item at rank k is a relevant document, zero otherwise Note that the average is over all relevant documents and the relevant documents not retrieved get a precision score of zero

Some authors choose to interpolate the p r function to reduce the impact of "wiggles" in the curve 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 :

AveP = 1 11 ∑ r ∈ p interp r =\sum _p_ r

where p interp r r is an interpolated precision that takes the maximum precision over all recalls greater than r :

p interp r = max r ~ : r ~ ≥ r ⁡ p r ~ r=\operatorname _:\geq rp

An alternative is to derive an analytical p r 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

Precision at K

For modern Web-scale information retrieval, recall is no longer a meaningful metric, as many queries have thousands of relevant documents, and few users will be interested in reading all of them Precision at k documents P@k is still a useful metric eg, P@10 or "Precision at 10" corresponds to the number of relevant results on the first search results page, but fails to take into account the positions of the relevant documents among the top k Another shortcoming is that on a query with fewer relevant results than k, even a perfect system will have a score less than 1 It is easier to score manually since only the top k results need to be examined to determine if they are relevant or not


R-precision requires knowing all documents that are relevant to a query The number of relevant documents, R , is used as the cutoff for calculation, and this varies from query to query For example, if there are 15 documents relevant to "red" in a corpus R=15, R-precision for "red" looks at the top 15 documents returned, counts the number that are relevant r turns that into a relevancy fraction: r / R = r / 15

Precision is equal to recall at the R-th position

Empirically, this measure is often highly correlated to mean average precision

Mean average precision

Mean average precision for a set of queries is the mean of the average precision scores for each query

MAP = ∑ q = 1 Q A v e P q Q =^\operatorname \!

where Q is the number of queries

Discounted cumulative gain

Main article: Discounted cumulative gain

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

The DCG accumulated at a particular rank position p is defined as:

D C G p = r e l 1 + ∑ i = 2 p r e l i log 2 ⁡ i =rel_+\sum _^i

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 I D C G p , which normalizes the score:

n D C G p = D C G p I D C G p =

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 D C G p will be the same as the I D C G p producing an nDCG of 10 All nDCG calculations are then relative values on the interval 00 to 10 and so are cross-query comparable

Other measures

Terminology and derivations
from a confusion matrix
true positive TP eqv with hit true negative TN eqv with correct rejection false positive FP eqv with false alarm, Type I error false negative FN eqv with miss, Type II error sensitivity or true positive rate TPR eqv with hit rate, recall T P R = T P P = T P T P + F N ==+ specificity SPC or true negative rate TNR S P C = T N N = T N F P + T N ==+ precision or positive predictive value PPV P P V = T P T P + F P =+ recall recall r e c a l l = T P T P + F N =+ negative predictive value NPV N P V = T N T N + F N =+ fall-out or false positive rate FPR F P R = F P N = F P F P + T N = 1 − S P C ==+=1- false discovery rate FDR F D R = F P F P + T P = 1 − P P V =+=1- miss rate or false negative rate FNR F N R = F N P = F N F N + T P = 1 − T P R ==+=1- accuracy ACC A C C = T P + T N P + N =+ F1 score is the harmonic mean of precision and sensitivity F 1 = 2 T P 2 T P + F P + F N =++ Matthews correlation coefficient MCC T P × T N − F P × F N T P + F P T P + F N T N + F P T N + F N

Informedness = Sensitivity + Specificity - 1
Markedness = Precision + NPV - 1

Sources: Fawcett 2006, Powers 2011, and Ting 2011

  • Mean reciprocal rank
  • Spearman's rank correlation coefficient
  • bpref - a summation-based measure of how many relevant documents are ranked before irrelevant documents
  • GMAP - geometric mean of per-topic average precision
  • Measures based on marginal relevance and document diversity - see Relevance information retrieval § Problems and alternatives


Visualizations of information retrieval performance include:

  • Graphs which chart precision on one axis and recall on the other
  • Histograms of average precision over various topics
  • Receiver operating characteristic ROC curve
  • Confusion matrix


  • Before the 1900s 1801: Joseph Marie Jacquard invents the Jacquard loom, the first machine to use punched cards to control a sequence of operations 1880s: Herman Hollerith invents an electro-mechanical data tabulator using punch cards as a machine readable medium 1890 Hollerith cards, keypunches and tabulators used to process the 1890 US Census data
  • 1920s-1930s 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
  • 1940s–1950s late 1940s: The US military confronted problems of indexing and retrieval of wartime scientific research documents captured from Germans 1945: Vannevar Bush's As We May Think appeared in Atlantic Monthly 1947: Hans Peter Luhn research engineer at IBM since 1941 began work on a mechanized punch card-based system for searching chemical compounds 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 1950: The term "information retrieval" was coined by Calvin Mooers 1951: Philip Bagley conducted the earliest experiment in computerized document retrieval in a master thesis at MIT 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 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 1959: Hans Peter Luhn published "Auto-encoding of documents for information retrieval"
  • 1960s: early 1960s: Gerard Salton began work on IR at Harvard, later moved to Cornell 1960: Melvin Earl Maron and John Lary Kuhns published "On relevance, probabilistic indexing, and information retrieval" in the Journal of the ACM 73:216–244, July 1960 1962:
    • 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
    • Kent published Information Analysis and Retrieval
    • 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
    • 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
    • Karen Spärck Jones finished her thesis at Cambridge, Synonymy and Semantic Classification, and continued work on computational linguistics as it applies to IR
    • 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 system
    • National Library of Medicine developed MEDLARS Medical Literature Analysis and Retrieval System, the first major machine-readable database and batch-retrieval system
    • Project Intrex at MIT
    1965: J C R Licklider published Libraries of the Future 1966: Don Swanson was involved in studies at University of Chicago on Requirements for Future Catalogs late 1960s: F Wilfrid Lancaster completed evaluation studies of the MEDLARS system and published the first edition of his text on information retrieval 1968:
    • Gerard Salton published Automatic Information Organization and Retrieval
    • John W Sammon, Jr's RADC Tech report "Some Mathematics of Information Storage and Retrieval" outlined the vector model
    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
  • 1970s early 1970s:
    • First online systems—NLM's AIM-TWX, MEDLINE; Lockheed's Dialog; SDC's ORBIT
    • Theodor Nelson promoting concept of hypertext, published Computer Lib/Dream Machines
    1971: Nicholas Jardine and Cornelis J van Rijsbergen published "The use of hierarchic clustering in information retrieval", which articulated the "cluster hypothesis" 1975: Three highly influential publications by Salton fully articulated his vector processing framework and term discrimination model:
    • A Theory of Indexing Society for Industrial and Applied Mathematics
    • A Theory of Term Importance in Automatic Text Analysis JASIS v 26
    • A Vector Space Model for Automatic Indexing CACM 18:11
    1978: The First ACM SIGIR conference 1979: C J van Rijsbergen published Information Retrieval Butterworths Heavy emphasis on probabilistic models 1979: Tamas Doszkocs implemented the CITE natural language user interface for MEDLINE at the National Library of Medicine The CITE system supported free form query input, ranked output and relevance feedback
  • 1980s 1980: First international ACM SIGIR conference, joint with British Computer Society IR group in Cambridge 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 1983: Salton and Michael J McGill published Introduction to Modern Information Retrieval McGraw-Hill, with heavy emphasis on vector models 1985: David Blair and Bill Maron publish: An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System mid-1980s: Efforts to develop end-user versions of commercial IR systems 1985–1993: Key papers on and experimental systems for visualization interfaces Work by Donald B Crouch, Robert R Korfhage, Matthew Chalmers, Anselm Spoerri and others 1989: First World Wide Web proposals by Tim Berners-Lee at CERN
  • 1990s 1992: First TREC conference 1997: Publication of Korfhage's Information Storage and Retrieval with emphasis on visualization and multi-reference point systems late 1990s: Web 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

Awards in the field

  • Tony Kent Strix award
  • Gerard Salton Award

Leading IR Research Groups

  • Center for Intelligent Information Retrieval CIIR at the University of Massachusetts Amherst
  • Information Retrieval Group at the University of Glasgow
  • Information and Language Processing Systems ILPS at the University of Amsterdam
  • Language Technologies Institutes LTI at the Carnegie Mellon University
  • Text Information Management and Analysis Group TIMAN at the University of Illinois at Urbana-Champaign

See also

  • Adversarial information retrieval
  • Collaborative information seeking
  • Controlled vocabulary
  • Cross-language information retrieval
  • Data mining
  • European Summer School in Information Retrieval
  • Human–computer information retrieval HCIR
  • Information extraction
  • Information Retrieval Facility
  • Knowledge visualization
  • Multimedia information retrieval
  • Personal information management
  • Relevance Information Retrieval
  • Relevance feedback
  • Rocchio Classification
  • Search index
  • Social information seeking
  • Special Interest Group on Information Retrieval
  • Subject indexing
  • Temporal information retrieval
  • tf-idf
  • XML-Retrieval


  1. ^ Jansen, B J and Rieh, S 2010 The Seventeen Theoretical Constructs of Information Searching and Information Retrieval Journal of the American Society for Information Sciences and Technology 618, 1517-1534
  2. ^ Goodrum, Abby A 2000 "Image Information Retrieval: An Overview of Current Research" Informing Science 3
  3. ^ Foote, Jonathan 1999 "An overview of audio information retrieval" Multimedia Systems Springer 
  4. ^ Beel, Jöran; Gipp, Bela; Stiller, Jan-Olaf 2009 Information Retrieval On Mind Maps - What Could It Be Good For Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing CollaborateCom'09 Washington, DC: IEEE 
  5. ^ Frakes, William B 1992 Information Retrieval Data Structures & Algorithms Prentice-Hall, Inc ISBN 0-13-463837-9 
  6. ^ a b Singhal, Amit 2001 "Modern Information Retrieval: A Brief Overview" PDF Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 24 4: 35–43 
  7. ^ Mark Sanderson & W Bruce Croft 2012 "The History of Information Retrieval Research" Proceedings of the IEEE 100: 1444–1451 doi:101109/jproc20122189916 
  8. ^ JE Holmstrom 1948 "'Section III Opening Plenary Session" The Royal Society Scientific Information Conference, 21 June-2 July 1948: report and papers submitted: 85 
  9. ^ a b Zhu, Mu 2004 "Recall, Precision and Average Precision" PDF 
  10. ^ Turpin, Andrew; Scholer, Falk 2006 "User performance versus precision measures for simple search tasks" Proceedings of the 29th Annual international ACM SIGIR Conference on Research and Development in information Retrieval Seattle, WA, August 06–11, 2006 New York, NY: ACM: 11–18 doi:101145/11481701148176 ISBN 1-59593-369-7 
  11. ^ a b Everingham, Mark; Van Gool, Luc; Williams, Christopher K I; Winn, John; Zisserman, Andrew June 2010 "The PASCAL Visual Object Classes VOC Challenge" PDF International Journal of Computer Vision Springer 88 2: 303–338 doi:101007/s11263-009-0275-4 Retrieved 2011-08-29 
  12. ^ a b Manning, Christopher D; Raghavan, Prabhakar; Schütze, Hinrich 2008 Introduction to Information Retrieval Cambridge University Press 
  13. ^ KH Brodersen, CS Ong, KE Stephan, JM Buhmann 2010 The binormal assumption on precision-recall curves Proceedings of the 20th International Conference on Pattern Recognition, 4263-4266
  14. ^ a b c Christopher D Manning, Prabhakar Raghavan and Hinrich Schütze 2009 "Chapter 8: Evaluation in information retrieval" PDF Retrieved 2015-06-14  CS1 maint: Uses authors parameter link Part of Introduction to Information Retrieval
  15. ^ a b c d e http://trecnistgov/pubs/trec15/appendices/CEMEASURES06pdf
  16. ^ Fawcett, Tom 2006 "An Introduction to ROC Analysis" PDF Pattern Recognition Letters 27 8: 861 – 874 doi:101016/jpatrec200510010 
  17. ^ Powers, David M W 2011 "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation" PDF Journal of Machine Learning Technologies 2 1: 37–63 
  18. ^ Ting, Kai Ming 2011 Encyclopedia of machine learning Springer ISBN 978-0-387-30164-8 
  19. ^ Mooers, Calvin N; The Theory of Digital Handling of Non-numerical Information and its Implications to Machine Economics Zator Technical Bulletin No 48, cited in Fairthorne, R A 1958 "Automatic Retrieval of Recorded Information" The Computer Journal 1 1: 37 doi:101093/comjnl/1136 
  20. ^ Doyle, Lauren; Becker, Joseph 1975 Information Retrieval and Processing Melville pp 410 pp ISBN 0-471-22151-1 
  21. ^ "Machine literature searching X Machine language; factors underlying its design and development" doi:101002/asi5090060411 
  22. ^ Maron, Melvin E 2008 "An Historical Note on the Origins of Probabilistic Indexing" PDF Information Processing and Management 44 2: 971–972 doi:101016/jipm200702012 
  23. ^ N Jardine, CJ van Rijsbergen December 1971 "The use of hierarchic clustering in information retrieval" Information Storage and Retrieval 7 5: 217–240 doi:101016/0020-02717190051-9 
  24. ^ Doszkocs, TE & Rapp, BA 1979 "Searching MEDLINE in English: a Prototype User Inter-face with Natural Language Query, Ranked Output, and relevance feedback," In: Proceedings of the ASIS Annual Meeting, 16: 131-139
  25. ^ Korfhage, Robert R 1997 Information Storage and Retrieval Wiley pp 368 pp ISBN 978-0-471-14338-3 
  26. ^ "Center for Intelligent Information Retrieval | UMass Amherst" ciircsumassedu Retrieved 2016-07-29 
  27. ^ "University of Glasgow - Schools - School of Computing Science - Research - Research overview - Information Retrieval" wwwglaacuk Retrieved 2016-07-29 
  28. ^ "ILPS - information and language processing systems" ILPS Retrieved 2016-07-29 

Further reading

  • Christopher D Manning, Prabhakar Raghavan, and Hinrich Schütze Introduction to Information Retrieval Cambridge University Press, 2008
  • Stefan Büttcher, Charles L A Clarke, and Gordon V Cormack Information Retrieval: Implementing and Evaluating Search Engines MIT Press, Cambridge, Mass, 2010

External links

  • ACM SIGIR: Information Retrieval Special Interest Group
  • BCS IRSG: British Computer Society - Information Retrieval Specialist Group
  • Text Retrieval Conference TREC
  • Forum for Information Retrieval Evaluation FIRE
  • Information Retrieval online book by C J van Rijsbergen
  • Information Retrieval Wiki
  • Information Retrieval Facility
  • Information Retrieval @ DUTH
  • TREC report on information retrieval evaluation techniques
  • How eBay measures search relevance
  • Information retrieval performance evaluation tool @ Athena Research Centre

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