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Fuzzy Retrieval
Fuzzy retrieval techniques are based on the Extended Boolean model and the Fuzzy set theory. There are two classical fuzzy retrieval models: Mixed Min and Max (MMM) and the Paice model. Both models do not provide a way of evaluating query weights, however this is considered by the P-norms algorithm. Mixed Min and Max model (MMM) In fuzzy-set theory, an element has a varying degree of membership, say ''dA'', to a given set ''A'' instead of the traditional membership choice (is an element/is not an element). In MMM each index term has a fuzzy set associated with it. A document's weight with respect to an index term ''A'' is considered to be the degree of membership of the document in the fuzzy set associated with ''A''. The degree of membership for union and intersection are defined as follows in Fuzzy set theory: :d_= min(d_A, d_B) :d_= max(d_A,d_B) According to this, documents that should be retrieved for a query of the form ''A or B'', should be in the fuzzy set associated with ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Extended Boolean Model
The Extended Boolean model was described in a Communications of the ACM article appearing in 1983, by Gerard Salton, Edward A. Fox, and Harry Wu. The goal of the Extended Boolean model is to overcome the drawbacks of the Boolean model that has been used in information retrieval. The Boolean model doesn't consider term weights in queries, and the result set of a Boolean query is often either too small or too big. The idea of the extended model is to make use of partial matching and term weights as in the vector space model. It combines the characteristics of the Vector Space Model with the properties of Boolean algebra and ranks the similarity between queries and documents. This way a document may be somewhat relevant if it matches some of the queried terms and will be returned as a result, whereas in the Standard Boolean model it wasn't. Thus, the extended Boolean model can be considered as a generalization of both the Boolean and vector space models; those two are special cases if ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fuzzy Set
In mathematics, fuzzy sets (a.k.a. uncertain sets) are sets whose elements have degrees of membership. Fuzzy sets were introduced independently by Lotfi A. Zadeh in 1965 as an extension of the classical notion of set. At the same time, defined a more general kind of structure called an ''L''-relation, which he studied in an abstract algebraic context. Fuzzy relations, which are now used throughout fuzzy mathematics and have applications in areas such as linguistics , decision-making , and clustering , are special cases of ''L''-relations when ''L'' is the unit interval , 1 In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition—an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval , 1 Fuzzy sets general ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Standard Boolean Model
The (standard) Boolean model of information retrieval (BIR) is a classical information retrieval (IR) model and, at the same time, the first and most-adopted one. It is used by many IR systems to this day. The BIR is based on Boolean logic and classical set theory in that both the documents to be searched and the user's query are conceived as sets of terms (a bag-of-words model). Retrieval is based on whether or not the documents contain the query terms. Definitions An ''index term'' is a word or expression'','' which may be stemmed, describing or characterizing a document, such as a keyword given for a journal article. LetT = \be the set of all such index terms. A ''document'' is any subset of T. LetD = \be the set of all documents. A ''query'' is a Boolean expression Q in normal form:Q = (W_1\ \or\ W_2\ \or\ \cdots) \and\ \cdots\ \and\ (W_i\ \or\ W_\ \or\ \cdots)where W_i is true for D_j when t_i \in D_j. (Equivalently, Q could be expressed in disjunctive normal form.) We ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Christopher D
Christopher is the English version of a Europe-wide name derived from the Greek name Χριστόφορος (''Christophoros'' or '' Christoforos''). The constituent parts are Χριστός (''Christós''), "Christ" or "Anointed", and φέρειν (''phérein''), "to bear"; hence the "Christ-bearer". As a given name, 'Christopher' has been in use since the 10th century. In English, Christopher may be abbreviated as "Chris", "Topher", and sometimes "Kit". It was frequently the most popular male first name in the United Kingdom, having been in the top twenty in England and Wales from the 1940s until 1995, although it has since dropped out of the top 100. The name is most common in England and not so common in Wales, Scotland, or Ireland. People with the given name Antiquity and Middle Ages * Saint Christopher (died 251), saint venerated by Catholics and Orthodox Christians * Christopher (Domestic of the Schools) (fl. 870s), Byzantine general * Christopher Lekapenos (died 931), B ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Precision And Recall
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Both precision and recall are therefore based on relevance. Consider a computer program for recognizing dogs (the relevant element) in a digital photograph. Upon processing a picture which contains ten cats and twelve dogs, the program identifies eight dogs. Of the eight elements identified as dogs, only five actually are dogs (true positives), while the other three are cats (false positives). Seven dogs were missed (false negatives), and seven cats were correctly excluded (true negatives). The program's precision is then 5/8 (true positives / ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
