Contents 1 Overview 2 History 3 Model types 3.1 First dimension: mathematical basis 3.2 Second dimension: properties of the model 4 Performance and correctness measures 4.1 Precision 4.2 Recall 4.3 Fall-out 4.4 F-score / F-measure 4.5 Average precision 4.6 Precision at K 4.7 R-Precision 4.8 Mean average precision 4.9 Discounted cumulative gain 4.10 Other measures 4.11 Visualization 5 Timeline 6 Major conferences 7 Awards in the field 8 Leading IR Research Groups 9 See also 10 References 11 Further reading 12 External links


Overview[edit] 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.[1] Depending on the application the data objects may be, for example, text documents, images,[2] audio,[3] mind maps[4] 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.[5]


History[edit] “ there is ... a machine called the Univac ... whereby letters and figures are coded as a pattern of magnetic spots on a long steel tape. By this means the text of a document, preceded by its subject code symbol, ca be recorded ... the machine ... automatically selects and types out those references which have been coded in any desired way at a rate of 120 words a minute ” —  J. E. Holmstrom, 1948 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.[6] 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.[7] The first description of a computer searching for information was described by Holmstrom in 1948,[8] 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).[6] 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[edit] 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[edit] 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 a.k.a. 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[edit] 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 (e.g. 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[edit] It has been suggested that this section be split out into another article titled Evaluation_measures_(information_retrieval). (Discuss) (June 2017) 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 (e.g., 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.[citation needed] The mathematical symbols used in the formulas below mean: X ∩ Y {\displaystyle X\cap Y} - Intersection - in this case, specifying the documents in both sets X and Y | X | {\displaystyle |X|} - Cardinality - in this case, the number of documents in set X ∫ {\displaystyle \int } - Integral ∑ {\displaystyle \sum } - Summation Δ {\displaystyle \Delta } - Symmetric difference Precision[edit] Main article: Precision and recall Precision is the fraction of the documents retrieved that are relevant to the user's information need. precision = | { relevant documents } ∩ { retrieved documents } | | { retrieved documents } | {\displaystyle {\mbox{precision}}={\frac {|\{{\mbox{relevant documents}}\}\cap \{{\mbox{retrieved documents}}\}|}{|\{{\mbox{retrieved documents}}\}|}}} 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. Recall[edit] Main article: Precision and recall Recall is the fraction of the documents that are relevant to the query that are successfully retrieved. recall = | { relevant documents } ∩ { retrieved documents } | | { relevant documents } | {\displaystyle {\mbox{recall}}={\frac {|\{{\mbox{relevant documents}}\}\cap \{{\mbox{retrieved documents}}\}|}{|\{{\mbox{relevant documents}}\}|}}} 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. Fall-out[edit] The proportion of non-relevant documents that are retrieved, out of all non-relevant documents available: fall-out = | { non-relevant documents } ∩ { retrieved documents } | | { non-relevant documents } | {\displaystyle {\mbox{fall-out}}={\frac {|\{{\mbox{non-relevant documents}}\}\cap \{{\mbox{retrieved documents}}\}|}{|\{{\mbox{non-relevant documents}}\}|}}} In binary classification, fall-out is closely related to specificity and is equal to ( 1 − specificity ) {\displaystyle (1-{\mbox{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[edit] 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 ) {\displaystyle F={\frac {2\cdot \mathrm {precision} \cdot \mathrm {recall} }{(\mathrm {precision} +\mathrm {recall} )}}} This is also known as the F 1 {\displaystyle F_{1}} measure, because recall and precision are evenly weighted. The general formula for non-negative real β {\displaystyle \beta } 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 ) {\displaystyle F_{\beta }={\frac {(1+\beta ^{2})\cdot (\mathrm {precision} \cdot \mathrm {recall} )}{(\beta ^{2}\cdot \mathrm {precision} +\mathrm {recall} )}}\,} Two other commonly used F measures are the F 2 {\displaystyle F_{2}} measure, which weights recall twice as much as precision, and the F 0.5 {\displaystyle F_{0.5}} measure, which weights precision twice as much as recall. The F-measure was derived by van Rijsbergen (1979) so that F β {\displaystyle F_{\beta }} "measures the effectiveness of retrieval with respect to a user who attaches β {\displaystyle \beta } 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 {\displaystyle E=1-{\frac {1}{{\frac {\alpha }{P}}+{\frac {1-\alpha }{R}}}}} . Their relationship is: F β = 1 − E {\displaystyle F_{\beta }=1-E} where α = 1 1 + β 2 {\displaystyle \alpha ={\frac {1}{1+\beta ^{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.[citation needed] Average precision[edit] 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 ) {\displaystyle p(r)} as a function of recall r {\displaystyle r} . Average precision computes the average value of p ( r ) {\displaystyle p(r)} over the interval from r = 0 {\displaystyle r=0} to r = 1 {\displaystyle r=1} :[9] AveP = ∫ 0 1 p ( r ) d r {\displaystyle \operatorname {AveP} =\int _{0}^{1}p(r)dr} 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 ) {\displaystyle \operatorname {AveP} =\sum _{k=1}^{n}P(k)\Delta r(k)} where k {\displaystyle k} is the rank in the sequence of retrieved documents, n {\displaystyle n} is the number of retrieved documents, P ( k ) {\displaystyle P(k)} is the precision at cut-off k {\displaystyle k} in the list, and Δ r ( k ) {\displaystyle \Delta r(k)} is the change in recall from items k − 1 {\displaystyle k-1} to k {\displaystyle k} .[9] This finite sum is equivalent to: AveP = ∑ k = 1 n ( P ( k ) × rel ⁡ ( k ) ) number of relevant documents {\displaystyle \operatorname {AveP} ={\frac {\sum _{k=1}^{n}(P(k)\times \operatorname {rel} (k))}{\mbox{number of relevant documents}}}\!} where rel ⁡ ( k ) {\displaystyle \operatorname {rel} (k)} is an indicator function equaling 1 if the item at rank k {\displaystyle k} is a relevant document, zero otherwise.[10] 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 ) {\displaystyle p(r)} function to reduce the impact of "wiggles" in the curve.[11][12] For example, the PASCAL Visual Object Classes challenge prior to 2010 (a benchmark for computer vision object detection, the evaluation metric changed after 2010 to effectively sample the curve at all unique recall values.) computes average precision by averaging the precision over a set of evenly spaced recall levels {0, 0.1, 0.2, ... 1.0}:[11][12] AveP = 1 11 ∑ r ∈ { 0 , 0.1 , … , 1.0 } p interp ( r ) {\displaystyle \operatorname {AveP} ={\frac {1}{11}}\sum _{r\in \{0,0.1,\ldots ,1.0\}}p_{\operatorname {interp} }(r)} where p interp ( r ) {\displaystyle p_{\operatorname {interp} }(r)} is an interpolated precision that takes the maximum precision over all recalls greater than r {\displaystyle r} : p interp ( r ) = max r ~ : r ~ ≥ r ⁡ p ( r ~ ) {\displaystyle p_{\operatorname {interp} }(r)=\operatorname {max} _{{\tilde {r}}:{\tilde {r}}\geq r}p({\tilde {r}})} . An alternative is to derive an analytical p ( r ) {\displaystyle 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.[13] Precision at K[edit] When the output of a classifier can be ordered (e.g. by some confidence measure), we can consider only the top k results and compute the precision for those. This is known as Precision at k, or P@k. For modern (Web-scale) information retrieval, for instance, 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 (e.g., 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.[citation needed] Another shortcoming is that on a query with fewer relevant results than k, even a perfect system will have a score less than 1.[14] 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[edit] R-precision requires knowing all documents that are relevant to a query. The number of relevant documents, R {\displaystyle 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 {\displaystyle r} turns that into a relevancy fraction: r / R = r / 15 {\displaystyle r/R=r/15} .[15] Precision is equal to recall at the R-th position.[14] Empirically, this measure is often highly correlated to mean average precision.[14] Mean average precision[edit] 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 {\displaystyle \operatorname {MAP} ={\frac {\sum _{q=1}^{Q}\operatorname {AveP(q)} }{Q}}\!} where Q is the number of queries. Discounted cumulative gain[edit] 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 {\displaystyle p} is defined as: D C G p = r e l 1 + ∑ i = 2 p r e l i log 2 ⁡ i . {\displaystyle \mathrm {DCG_{p}} =rel_{1}+\sum _{i=2}^{p}{\frac {rel_{i}}{\log _{2}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 {\displaystyle IDCG_{p}} ), which normalizes the score: n D C G p = D C G p I D C G p . {\displaystyle \mathrm {nDCG_{p}} ={\frac {DCG_{p}}{IDCG{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 {\displaystyle DCG_{p}} will be the same as the I D C G p {\displaystyle IDCG_{p}} 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. Other measures[edit] Terminology and derivations from a confusion matrix condition positive (P) the number of real positive cases in the data condition negatives (N) the number of real negative cases in the data 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, recall, hit rate, or true positive rate (TPR) T P R = T P P = T P T P + F N {\displaystyle \mathrm {TPR} ={\frac {\mathrm {TP} }{P}}={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FN} }}} specificity or true negative rate (TNR) T N R = T N N = T N T N + F P {\displaystyle \mathrm {TNR} ={\frac {\mathrm {TN} }{N}}={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FP} }}} precision or positive predictive value (PPV) P P V = T P T P + F P {\displaystyle \mathrm {PPV} ={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FP} }}} negative predictive value (NPV) N P V = T N T N + F N {\displaystyle \mathrm {NPV} ={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FN} }}} miss rate or false negative rate (FNR) F N R = F N P = F N F N + T P = 1 − T P R {\displaystyle \mathrm {FNR} ={\frac {\mathrm {FN} }{P}}={\frac {\mathrm {FN} }{\mathrm {FN} +\mathrm {TP} }}=1-\mathrm {TPR} } fall-out or false positive rate (FPR) F P R = F P N = F P F P + T N = 1 − T N R {\displaystyle \mathrm {FPR} ={\frac {\mathrm {FP} }{N}}={\frac {\mathrm {FP} }{\mathrm {FP} +\mathrm {TN} }}=1-\mathrm {TNR} } false discovery rate (FDR) F D R = F P F P + T P = 1 − P P V {\displaystyle \mathrm {FDR} ={\frac {\mathrm {FP} }{\mathrm {FP} +\mathrm {TP} }}=1-\mathrm {PPV} } false omission rate (FOR) F O R = F N F N + T N = 1 − N P V {\displaystyle \mathrm {FOR} ={\frac {\mathrm {FN} }{\mathrm {FN} +\mathrm {TN} }}=1-\mathrm {NPV} } accuracy (ACC) A C C = T P + T N P + N = T P + T N T P + T N + F P + F N {\displaystyle \mathrm {ACC} ={\frac {\mathrm {TP} +\mathrm {TN} }{P+N}}={\frac {\mathrm {TP} +\mathrm {TN} }{\mathrm {TP} +\mathrm {TN} +\mathrm {FP} +\mathrm {FN} }}} F1 score is the harmonic mean of precision and sensitivity F 1 = 2 ⋅ P P V ⋅ T P R P P V + T P R = 2 T P 2 T P + F P + F N {\displaystyle F_{1}=2\cdot {\frac {\mathrm {PPV} \cdot \mathrm {TPR} }{\mathrm {PPV} +\mathrm {TPR} }}={\frac {2\mathrm {TP} }{2\mathrm {TP} +\mathrm {FP} +\mathrm {FN} }}} Matthews correlation coefficient (MCC) M C C = T P × T N − F P × F N ( T P + F P ) ( T P + F N ) ( T N + F P ) ( T N + F N ) {\displaystyle \mathrm {MCC} ={\frac {\mathrm {TP} \times \mathrm {TN} -\mathrm {FP} \times \mathrm {FN} }{\sqrt {(\mathrm {TP} +\mathrm {FP} )(\mathrm {TP} +\mathrm {FN} )(\mathrm {TN} +\mathrm {FP} )(\mathrm {TN} +\mathrm {FN} )}}}} Informedness or Bookmaker Informedness (BM) B M = T P R + T N R − 1 {\displaystyle \mathrm {BM} =\mathrm {TPR} +\mathrm {TNR} -1} Markedness (MK) M K = P P V + N P V − 1 {\displaystyle \mathrm {MK} =\mathrm {PPV} +\mathrm {NPV} -1} Sources: Fawcett (2006), Powers (2011), and Ting (2011) [16] [17] [18] Mean reciprocal rank Spearman's rank correlation coefficient bpref - a summation-based measure of how many relevant documents are ranked before irrelevant documents[15] GMAP - geometric mean of (per-topic) average precision[15] Measures based on marginal relevance and document diversity - see Relevance (information retrieval) § Problems and alternatives Visualization[edit] Visualizations of information retrieval performance include: Graphs which chart precision on one axis and recall on the other[15] Histograms of average precision over various topics[15] Receiver operating characteristic (ROC curve) Confusion matrix


Timeline[edit] 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.[19] 1951: Philip Bagley conducted the earliest experiment in computerized document retrieval in a master thesis at MIT.[20] 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.[21] 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[22] published "On relevance, probabilistic indexing, and information retrieval" in the Journal of the ACM 7(3):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. 1963: 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). 1964: 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. mid-1960s: 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."[23] 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.[24] 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[25] with emphasis on visualization and multi-reference point systems. 1999: Publication of Ricardo Baeza-Yates and Berthier Ribeiro-Neto's Modern Information Retrieval by Addison Wesley, the first book that attempts to cover all IR. 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.


Major conferences[edit] SIGIR: Conference on Research and Development in Information Retrieval ECIR: European Conference on Information Retrieval CIKM: Conference on Information and Knowledge Management WWW: International World Wide Web Conference WSDM: Conference on Web Search and Data Mining ICTIR: International Conference on Theory of Information Retrieval


Awards in the field[edit] Tony Kent Strix award Gerard Salton Award


Leading IR Research Groups[edit] Center for Intelligent Information Retrieval (CIIR) at the University of Massachusetts Amherst [26] Information Retrieval Group at the University of Glasgow [27] Information and Language Processing Systems (ILPS) at the University of Amsterdam [28] Information Retrieval Group (THUIR) at Tsinghua University [29]


See also[edit] 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


References[edit] ^ 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. 61(8), 1517-1534. ^ Goodrum, Abby A. (2000). "Image Information Retrieval: An Overview of Current Research". Informing Science. 3 (2).  ^ Foote, Jonathan (1999). "An overview of audio information retrieval". Multimedia Systems. Springer.  ^ 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.  ^ Frakes, William B.; Baeza-Yates, Ricardo (1992). Information Retrieval Data Structures & Algorithms. Prentice-Hall, Inc. ISBN 0-13-463837-9. Archived from the original on 2013-09-28.  ^ 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.  ^ Mark Sanderson & W. Bruce Croft (2012). "The History of Information Retrieval Research". Proceedings of the IEEE. 100: 1444–1451. doi:10.1109/jproc.2012.2189916.  ^ JE Holmstrom (1948). "'Section III. Opening Plenary Session". The Royal Society Scientific Information Conference, 21 June-2 July 1948: report and papers submitted: 85.  ^ a b Zhu, Mu (2004). "Recall, Precision and Average Precision" (PDF).  ^ Turpin, Andrew; Scholer, Falk (2006). "User performance versus precision measures for simple search tasks" (PDF). 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:10.1145/1148170.1148176. ISBN 1-59593-369-7.  ^ 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:10.1007/s11263-009-0275-4. Archived from the original (PDF) on 2011-11-20. Retrieved 2011-08-29.  ^ a b Manning, Christopher D.; Raghavan, Prabhakar; Schütze, Hinrich (2008). Introduction to Information Retrieval. Cambridge University Press.  ^ K.H. Brodersen, C.S. Ong, K.E. Stephan, J.M. Buhmann (2010). The binormal assumption on precision-recall curves Archived 2012-12-08 at the Wayback Machine.. Proceedings of the 20th International Conference on Pattern Recognition, 4263-4266. ^ 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 [1] ^ a b c d e http://trec.nist.gov/pubs/trec15/appendices/CE.MEASURES06.pdf ^ Fawcett, Tom (2006). "An Introduction to ROC Analysis" (PDF). Pattern Recognition Letters. 27 (8): 861–874. doi:10.1016/j.patrec.2005.10.010.  ^ 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.  ^ Ting, Kai Ming (2011). Encyclopedia of machine learning. Springer. ISBN 978-0-387-30164-8.  ^ 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:10.1093/comjnl/1.1.36.  ^ Doyle, Lauren; Becker, Joseph (1975). Information Retrieval and Processing. Melville. pp. 410 pp. ISBN 0-471-22151-1.  ^ "Machine literature searching X. Machine language; factors underlying its design and development". doi:10.1002/asi.5090060411.  ^ Maron, Melvin E. (2008). "An Historical Note on the Origins of Probabilistic Indexing" (PDF). Information Processing and Management. 44 (2): 971–972. doi:10.1016/j.ipm.2007.02.012.  ^ N. Jardine, C.J. van Rijsbergen (December 1971). "The use of hierarchic clustering in information retrieval". Information Storage and Retrieval. 7 (5): 217–240. doi:10.1016/0020-0271(71)90051-9.  ^ Doszkocs, T.E. & Rapp, B.A. (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. ^ Korfhage, Robert R. (1997). Information Storage and Retrieval. Wiley. pp. 368 pp. ISBN 978-0-471-14338-3.  ^ "Center for Intelligent Information Retrieval | UMass Amherst". ciir.cs.umass.edu. 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Further reading[edit] Ricardo Baeza-Yates, Berthier Ribeiro-Neto. Modern Information Retrieval: The Concepts and Technology behind Search (second edition). Addison-Wesley, UK, 2011. Stefan Büttcher, Charles L. A. Clarke, and Gordon V. Cormack. Information Retrieval: Implementing and Evaluating Search Engines. MIT Press, Cambridge, Mass., 2010. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.


External links[edit] Wikiquote has quotations related to: Information retrieval 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 Authority control GND: 4072803-1 NDL: 00575010 Retrieved from "https://en.wikipedia.org/w/index.php?title=Information_retrieval&oldid=815034293" Categories: Information retrievalNatural language processingHidden categories: Webarchive template wayback linksCS1 maint: Uses authors parameterArticles to be split from June 2017All articles to be splitAll articles with unsourced statementsArticles with unsourced statements from June 2015Pages using div col without cols and colwidth parametersWikipedia articles with GND identifiersArticles with inconsistent citation formats


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