Divergence-from-randomness Model
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Divergence-from-randomness Model
In the field of information retrieval, divergence from randomness, one of the first models, is one type of probabilistic model. It is basically used to test the amount of information carried in the documents. It is based on Harter's 2-Poisson indexing-model. The 2-Poisson model has a hypothesis that the level of the documents is related to a set of documents which contains words occur relatively greater than the rest of the documents. It is not a 'model', but a framework for weighting terms using probabilistic methods, and it has a special relationship for term weighting based on notion of eliteness. Term weights are being treated as the standard of whether a specific word is in that set or not. Term weights are computed by measuring the divergence between a term distribution produced by a random process and the actual term distribution. Divergence from randomness models set up by instantiating the three main components of the framework: first selecting a basic randomness model, ...
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Information Retrieval
Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds. Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; stores and manages those documents. Web search engines are the most visible IR applications. Overview An information retrieval process begins when a user or searcher enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In inf ...
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Probabilistic
Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty."Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory", Alan Stuart and Keith Ord, 6th Ed, (2009), .William Feller, ''An Introduction to Probability Theory and Its Applications'', (Vol 1), 3rd Ed, (1968), Wiley, . The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written ...
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Uniform Resource Name
A Uniform Resource Name (URN) is a Uniform Resource Identifier (URI) that uses the scheme. URNs are globally unique persistent identifiers assigned within defined namespaces so they will be available for a long period of time, even after the resource which they identify ceases to exist or becomes unavailable. URNs cannot be used to directly locate an item and need not be resolvable, as they are simply templates that another parser may use to find an item. URIs, URNs, and URLs URNs were originally conceived to be part of a three-part information architecture for the Internet, along with Uniform Resource Locators (URLs) and Uniform Resource Characteristics (URCs), a metadata framework. As described in RFC 1737 (1994), and later in RFC 2141 (1997), URNs were distinguished from URLs, which identify resources by specifying their locations in the context of a particular access protocol, such as HTTP or FTP. In contrast, URNs were conceived as persistent, location-independent ident ...
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