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Information retrieval(IR) is the science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within databases, whether relational stand-alone databases or hypertext networked databases such as the Internet or intranets, for text, sound, images or data. There is a common confusion, however, between data retrieval, document retrieval, information retrieval, and text retrieval, and each of these has its own bodies of literature, theory, praxis and technologies. IR is like most nascent fields interdisciplinary, based in computer science, library science, information science, cognitive psychology, linguistics, and statistics. Automated IR systems are used to reduce information overload. Many universities and public libraries use IR systems to provide access to books, journals, and other documents. IR systems are often related to object and query. Queries are formal statements of information needs that are put to an IR system by the user. An object is an entity which keeps or stores information in a database. User queries are matched to documents stored in a database. A document is, therefore, a data object. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates. 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 such a huge evaluation of text retrieval methodologies. Web search engines such as Google and Lycos are the most visible IR applications. Performance measures There are various ways to measure how well the retrieved information matches the intended information: The formulas for precision, recall and fall-out are translated from the german Wikipedia-article "Recall und Precision". See also this nice intuitive, graphical depiction. Precision The proportion of retrieved and relevant documents to all the documents retrieved: In binary classification, precision is analogous to positive predictive value. Precision can also be evaluated at a given cut-off rank, denoted P@n, instead of all retrieved documents. 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 technology. Recall The proportion of relevant documents that are retrieved, out of all relevant documents available: In binary classification, recall is called sensitivity. Fall-Out The probability to find an irrelevant among the retrieved documents. F-measure The weighted harmonic mean of precision and recall, the traditional F-measure or balanced F-score is: This is also known as the measure, because recall and precision are evenly weighted. The general formula for non-negative real α is: Two other commonly used F measures are the measure, which weights precision twice as much as recall, and the measure, which weights recall twice as much as precision. Mean average precision Over a set of queries, find the mean of the average precisions, where Average Precision is the average of the precision after each relevant document is retrieved. Where r is the rank, N the number retrieved, rel() a binary function on the relevance of a given rank, and P() precision at a given cut-off rank: This method emphasizes returning more relevant documents earlier. Model types of IR-models (translated from http://de.wikipedia.org/wiki/Informationsrückgewinnung#Klassifikation_von_Modellen_zur_Repr.C3.A4sentation_nat.C3.BCrlichsprachlicher_Dokumente German entry, original source http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=0514&lng=eng&id= Dominik Kuropka) For successful IR, it is necessary to represent the documents in some way. There are a number of models for this purpose. They can be categorized according to two dimensions like shown in the figure on the right: the mathematical basis and the properties of the model. (translated from German entry, original source Dominik Kuropka) First dimension: mathematical basis Second dimension: properties of the model Open source information retrieval systems Other retrieval tools Major Information retrieval research groups Major figures in information retrieval Other figures associated to information retrieval Awards in this field: Tony Kent Strix award. ACM SIGIR Gerard Salton Award 1983 - Gerard Salton, Cornell University"About the future of automatic information retrieval" 1988 - Karen Sparck Jones, University of Cambridge"A look back and a look forward" 1991 - Cyril Cleverdon, Cranfield Institute of Technology"The significance of the Cranfield tests on index languages" 1994 - William S. Cooper, University of California, Berkeley"The formalism of probability theory in IR: a foundation or an encumbrance?" 1997 - Tefko Saracevic, Rutgers University"Users lost: reflections on the past, future, and limits of information science" 2000 - Stephen E. Robertson, City University, London"On theoretical argument in information retrieval" 2003 - W. Bruce Croft, University of Massachusetts, Amherst"Information retrieval and computer science: an evolving relationship" 2006 - C. J. van Rijsbergen, University of Glasgow, UK"Quantum haystacks" See also | |||||||
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