Information retrieval (IR) is the science of searching for documents, for information Information, in its most restricted technical sense, is an ordered sequence of symbols. As a concept, however, information has many meanings. Moreover, the concept of information is closely related to notions of constraint, communication, control, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation within documents, and for metadata Metadata is "data about data", of any sort in any media. Metadata is text, voice, or image that describes what the audience wants or needs to see or experience. The audience could be a person, group, or software program. Metadata is important because it aids in clarifying and finding the actual data. An item of metadata may describe an about documents, as well as that of searching relational databases Such a grouping uses the relational model . Hence, such a database is called a "relational database." and the World Wide Web The World Wide Web, abbreviated as WWW and commonly known as the Web, is a system of interlinked hypertext documents accessed via the Internet. With a web browser, one can view web pages that may contain text, images, videos, and other multimedia and navigate between them by using hyperlinks. Using concepts from earlier hypertext systems, British. There is overlap in the usage of the terms data In computer science, data is anything in a form suitable for use with a computer. Data is often distinguished from programs. A program is a set of instructions that detail a task for the computer to perform. In this sense, data is thus everything that is not program code retrieval, document retrieval Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text, such as newspaper articles, real estate records or paragraphs in a manual. User queries can range from multi-sentence full descriptions of an information need to a few words, information retrieval, and text retrieval Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text, such as newspaper articles, real estate records or paragraphs in a manual. User queries can range from multi-sentence full descriptions of an information need to a few words, but each also has its own body of literature, theory, praxis Praxis is the process by which a theory, lesson, or skill is enacted or practiced, embodied and/or realized. It is a practical and applied knowledge to one's actions. It has meaning in political, educational, and spiritual realms, and technologies. IR is interdisciplinary An interdisciplinary field is a field of study that crosses traditional boundaries between academic disciplines or schools of thought, as new needs and professions have emerged, based on computer science Computer science or computing science is the study of the theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. It is frequently described as the systematic study of algorithmic processes that create, describe, and transform information. Computer science, mathematics Mathematics is the study of quantity, structure, space, and change. Mathematicians seek out patterns, formulate new conjectures, and establish truth by rigorous deduction from appropriately chosen axioms and definitions, library science Library science is an interdisciplinary field that applies the practices, perspectives, and tools of management, information technology, education, and other areas to libraries; the collection, organization, preservation, and dissemination of information resources; and the political economy of information. The first school for library science was, information science Information science is an interdisciplinary science primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information. Practitioners within the field study the application and usage of knowledge in organizations, along with the interaction between people, organizations and any, information architecture Information architecture is the art of expressing a model or concept of information used in activities that require explicit details of complex systems. Among these activities are library systems, Content Management Systems, web development, user interactions, database development, programming, technical writing, enterprise architecture, and, cognitive psychology Cognitive psychology is a discipline within psychology that investigates the internal mental processes of thought such as visual processing, memory, thinking, learning, feeling, problem solving, and language, linguistics Linguistics is the scientific study of natural language. Linguistics encompasses a number of sub-fields. An important topical division is between the study of language structure and the study of meaning (semantics and pragmatics). Grammar encompasses morphology (the formation and composition of words), syntax (the rules that determine how words, and statistics Statistics is the formal science of making effective use of numerical data relating to groups of individuals or experiments. It deals with all aspects of this, including not only the collection, analysis and interpretation of such data, but also the planning of the collection of data, in terms of the design of surveys and experiments.

Automated information retrieval systems are used to reduce what has been called "information overload "Information overload" is a term popularized by Alvin Toffler that refers to the difficulty a person can have understanding an issue and making decisions that can be caused by the presence of too much information. The term itself is mentioned in a 1964 book by Bertram Gross, The Managing of Organizations". Many universities and public libraries A public library is a library which is accessible by the public and is generally funded from public sources (such as tax money) and may be operated by civil servants. Taxing bodies for public libraries may be at any level from local to national central government level use IR systems to provide access to books, journals and other documents. Web search engines A web search engine is designed to search for information on the World Wide Web. The search results are usually presented in a list of results and are commonly called hits. The information may consist of web pages, images, information and other types of files. Some search engines also mine data available in databases or open directories. Unlike are the most visible IR applications.

History

The idea of using computers to search for relevant pieces of information was popularized in the article As We May Think As We May Think is an essay by Vannevar Bush, first published in The Atlantic Monthly in July 1945, and republished again as an abridged version in September 1945 — therefore, before and after the U.S. nuclear attacks on Japan. Bush expresses his concern for the direction of scientific efforts towards destruction, rather than understanding, and by Vannevar Bush Vannevar Bush was an American engineer and science administrator known for his work on analog computing, his political role in the development of the atomic bomb as a primary organizer of the Manhattan Project, and the idea of the memex, an adjustable microfilm-viewer which is somewhat analogous to the structure of the World Wide Web. As Director in 1945.^[1] 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).^[1]. 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 The National Institute of Standards and Technology , known between 1901 and 1988 as the National Bureau of Standards (NBS), is a measurement standards laboratory which is a non-regulatory agency of the United States Department of Commerce. The institute's official mission is: (NIST), cosponsored the Text Retrieval Conference The Text REtrieval Conference is an on-going series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks. It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Disruptive Technology Office of the U.S. Department of Defense, and began in 1992 as part of the TIPSTER Text (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 In telecommunications and software engineering, scalability is a desirable property of a system, a network, or a process, which indicates its ability to either handle growing amounts of work in a graceful manner or to be readily enlarged. For example, it can refer to the capability of a system to increase total throughput under an increased load to huge corpora. The introduction of web search engines A web search engine is designed to search for information on the World Wide Web. The search results are usually presented in a list of results and are commonly called hits. The information may consist of web pages, images, information and other types of files. Some search engines also mine data available in databases or open directories. Unlike has boosted the need for very large scale retrieval systems even further.

The use of digital methods for storing and retrieving information has led to the phenomenon of digital obsolescence Digital obsolescence is a situation where a digital resource is no longer readable because the physical media, the reader required to read the media, the hardware, or the software that runs on it, is no longer available. A prime example of this is the BBC Domesday Project. Cornell University Library’s digital preservation tutorial has a timeline, where a digital resource ceases to be readable because the physical media, the reader required to read the media, the hardware, or the software that runs on it, is no longer available. The information is initially easier to retrieve than if it were on paper, but is then effectively lost.

Timeline

• Before the 1900s

  1880s: Herman Hollerith Herman Hollerith was a German-American statistician who developed a mechanical tabulator based on punched cards to rapidly tabulate statistics from millions of pieces of data. He was the founder of the company that became IBM invents the recording of data on a machine readable medium.

  1890 Hollerith cards A punched card is a piece of stiff paper that contains digital information represented by the presence or absence of holes in predefined positions. Now almost an obsolete recording medium, punched cards were widely used throughout the 19th century for controlling textile looms and in the late 19th and early 20th century for operating fairground, key punches A keypunch is a device for manually entering data into punched cards by precisely punching holes at locations designated by the keys struck by the operator. Early keypunches were manual devices. Later keypunches were mechanized, often resembled a small desk, with a keyboard similar to a typewriter, and with hoppers for blank cards and stackers for and tabulators The tabulating machine was an electrical device designed to assist in summarizing information and, later, accounting. Invented by Herman Hollerith, the machine was developed to help process data for the 1890 U.S. Census. It spawned a larger class of devices known as unit record equipment and the data processing industry used to process the 1890 US Census The Eleventh United States Census was taken June 2, 1890. Most of the 1890 census was destroyed in 1921 during a fire in the basement of the Commerce Building in Washington, D.C data.

• 1940s–1950s

  late-1940s: The US military confronted problems of indexing and retrieval of wartime scientific research documents captured from Germans.

  1945: Vannevar Bush Vannevar Bush was an American engineer and science administrator known for his work on analog computing, his political role in the development of the atomic bomb as a primary organizer of the Manhattan Project, and the idea of the memex, an adjustable microfilm-viewer which is somewhat analogous to the structure of the World Wide Web. As Director's As We May Think As We May Think is an essay by Vannevar Bush, first published in The Atlantic Monthly in July 1945, and republished again as an abridged version in September 1945 — therefore, before and after the U.S. nuclear attacks on Japan. Bush expresses his concern for the direction of scientific efforts towards destruction, rather than understanding, and appeared in Atlantic Monthly The Atlantic is an American magazine founded in Boston, Massachusetts, in 1857. It was created as a literary and cultural commentary magazine. Though based in Boston, it quickly achieved a national reputation, which it held for more than a century. It was important for recognizing and publishing new writers and poets, and encouraging major careers.

  1947: Hans Peter Luhn Hans Peter Luhn was a computer scientist for IBM, and creator of the Luhn algorithm and KWIC (Key Words In Context) indexing. He was awarded over 80 patents. Luhn was born in Barmen, Germany (now part of Wuppertal) on July 1, 1896. After he completed secondary school, Luhn moved to Switzerland to learn the printing trade so he could join the (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 Eugene "Gene" Garfield is an American scientist, one of the founders of bibliometrics and scientometrics. He received a PhD in Structural Linguistics from the University of Pennsylvania in 1961. Dr. Garfield was the founder of the Institute for Scientific Information (ISI), which was located in Philadelphia, Pennsylvania. ISI now forms a).

  1950: The term "information retrieval" appears to have been coined by Calvin Mooers Calvin Northrup Mooers , was an American computer scientist known for his work in information retrieval and for the programming language TRAC.

  1951: Philip Bagley conducted the earliest experiment in computerized document retrieval in a master thesis at MIT The Massachusetts Institute of Technology is a private research university located in Cambridge, Massachusetts. MIT has five schools and one college, containing a total of 32 academic departments, with a strong emphasis on scientific and technological research. MIT is one of two private land-grant universities[b] and is also a sea-grant and space-.^[2]

  1955: Allen Kent joined Case Western Reserve University Case Western Reserve University is a private research university located in Cleveland, Ohio, USA. The university was created in 1967 by the federation of Case Institute of Technology (founded in 1881 by philanthropist Leonard Case Jr.) and Western Reserve University (founded in 1826 in the area that was once the Connecticut Western Reserve). TIME, and eventually becomes associate director of the Center for Documentation and Communications Research. That same year, Kent and colleagues publish a paper in American Documentation describing the precision and recall measures, as well as detailing a proposed "framework" for evaluating an IR system, which includes 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 Gerard Salton was a Professor of Computer Science at Cornell University. Salton was perhaps the leading computer scientist working in the field of information retrieval during his time. His group at Cornell developed the SMART Information Retrieval System began work on IR at Harvard, later moved to Cornell.

  1960: Melvin Earl (Bill) Maron and John Lary Kuhns^[3] 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 Alvin Martin Weinberg was an American nuclear physicist who was the administrator at Oak Ridge National Laboratory (ORNL) during in Manhattan Project period. He came to Oak Ridge, Tennessee in 1945 and remained there until his death in 2006.
  • Joseph Becker and Robert M. Hayes Robert M. Hayes is Professor Emeritus and former dean of the School of Library Service (1974-1989), now the Graduate School of Education and Information Studies at UCLA. He jointly taught mathematics and information science. Hayes was president of the American Society for Information Science and Technology, formerly known as the American 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 Karen Spärck Jones FBA was a British computer scientist finished her thesis at Cambridge, Synonymy and Semantic Classification, and continued work on computational linguistics Computational linguistics is an interdisciplinary field dealing with the statistical and/or rule-based modeling of natural language from a computational perspective. This modeling is not limited to any particular field of linguistics. Traditionally, computational linguistics was usually performed by computer scientists who had specialized in the as it applies to IR.
  • The National Bureau of Standards The National Institute of Standards and Technology , known between 1901 and 1988 as the National Bureau of Standards (NBS), is a measurement standards laboratory which is a non-regulatory agency of the United States Department of Commerce. The institute's official mission is: 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 Joseph Carl Robnett Licklider , known simply as J.C.R. or "Lick" was an American computer scientist, considered one of the most important figures in computer science and general computing history published Libraries of the Future.

  1966: Don Swanson Don R. Swanson is an American information scientist, most known for his work in literature-based discovery in the biomedical domain. His particular method has been used as a model for further work, and is often referred to as Swanson linking. He has been professor emeritus of the University of Chicago since 1996, but remains active in a post- 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." (Information Storage and Retrieval, 7(5), pp. 217–240, December 1971)

  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.

• 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.

  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^[4] 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, research, and implementation.

Overview

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 database. User queries are matched against the database information. Depending on the application the data objects may be, for example, text documents, images, 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 match 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]

Performance measures

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.

Precision

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

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 technology.

Recall

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

In binary classification, recall is 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

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

In binary classification, fall-out is closely related to specificity (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-measure

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

This is also known as the F₁ measure, because recall and precision are evenly weighted.

The general formula for non-negative real β is:

.

Two other commonly used F measures are the F₂ measure, which weights recall twice as much as precision, and the 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 "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 where α = 1 / (β² + 1).

Mean 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. Average precision emphasizes ranking relevant documents higher. It is the average of precisions computed at the point of each of the relevant documents in the ranked sequence:

where r is the rank, N the number retrieved, rel() a binary function on the relevance of a given rank, and P(r) precision at a given cut-off rank:

This metric is also sometimes referred to geometrically as the area under the Precision-Recall curve.

Note that the denominator (number of relevant documents) is the number of relevant documents in the entire collection, so that the metric reflects performance over all relevant documents, regardless of a retrieval cutoff. See: ^[6].

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:

Since result set may vary in size among different queries or systems, to compare performances the normalised version of DCG uses an ideal DCG - by sorting documents of a result list by relevance - to normalize the score:

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 DCG_p will be the same as the 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

• Mean reciprocal rank
• Spearman's rank correlation coefficient

Model types

For the information retrieval to be efficient, the documents are typically transformed into a suitable representation. There are several representations. 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
• Machine-learned ranking models view documents as vectors of ranking features (some of which often incorporate other ranking models mentioned above) and try to find the best way to combine these features into a single relevance score by machine learning methods.

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 (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 relay an external source for the degree of interdependency between two terms. (For example a human or sophisticated algorithms.)

Major figures

• Thomas Bayes
• Claude E. Shannon
• Gerard Salton
• Hans Peter Luhn
• W. Bruce Croft
• Karen Spärck Jones
• C. J. van Rijsbergen
• Stephen E. Robertson
• Ricardo Baeza-Yates

Awards in the field

• Tony Kent Strix award
• Gerard Salton Award

See also

References

1. ^ ^{a b} Singhal, Amit (2001). "Modern Information Retrieval: A Brief Overview". Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 24 (4): 35–43. http://singhal.info/ieee2001.pdf.
2. ^ Doyle, Lauren; Becker, Joseph (1975). Information Retrieval and Processing. Melville. pp. 410 pp.. ISBN 0471221511.
3. ^ Maron, Melvin E. (2008). "An Historical Note on the Origins of Probabilistic Indexing". Information Processing and Management 44: 971–972. http://yunus.hacettepe.edu.tr/~tonta/courses/spring2008/bby703/maron-on-probabilistic%20indexing-2008.pdf.
4. ^ Korfhage, Robert R. (1997). Information Storage and Retrieval. Wiley. pp. 368 pp.. ISBN 978-0-471-14338-3. http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471143383,descCd-authorInfo.html.
5. ^ Frakes, William B. (1992). Information Retrieval Data Structures & Algorithms. Prentice-Hall, Inc.. ISBN 0-13-463837-9. http://www.scribd.com/doc/13742235/Information-Retrieval-Data-Structures-Algorithms-William-B-Frakes.
6. ^ Turpin, Andrew; Scholer, Falk. "User performance versus precision measures for simple search tasks". Proceedings of the 29^th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Seattle, Washington, USA, August 06-11, 2006) (New York, NY: ACM): 11–18. doi:10.1145/1148170.1148176.

External links

• ACM SIGIR: Information Retrieval Special Interest Group
• BCS IRSG: British Computer Society - Information Retrieval Specialist Group
• Text Retrieval Conference (TREC)
• Chinese Web Information Retrieval Forum (CWIRF)
• Information Retrieval (online book) by C. J. van Rijsbergen
• Information Retrieval Wiki
• Information Retrieval Facility
• Introduction to Information Retrieval (online book) by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press. 2008.

Categories: Information retrieval | Natural language processing

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