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Context-free grammar

Context-free grammar
V → w where V is a single nonterminal symbol, and w is a string of terminals and/or nonterminals (w can be empty). A formal grammar is considered "context free" when its production rules can be applied regardless of the context of a nonterminal. No matter which symbols surround it, the single nonterminal on the left hand side can always be replaced by the right hand side. Context-free grammars arise in linguistics where they are used to describe the structure of sentences and words in natural language, and they were in fact invented by the Linguist Noam Chomsky for this purpose, but have not really lived up to their original expectation. By contrast, in computer science, as the use of recursively defined concepts increased, they were used more and more. In linguistics, some authors use the term phrase structure grammar to refer to context-free grammars, whereby phrase structure grammars are distinct from dependency grammars. Background[edit] can be logically parenthesized as follows: where

Programming language The earliest programming languages preceded the invention of the digital computer and were used to direct the behavior of machines such as Jacquard looms and player pianos.[1] Thousands of different programming languages have been created, mainly in the computer field, and many more still are being created every year. Many programming languages require computation to be specified in an imperative form (i.e., as a sequence of operations to perform), while other languages utilize other forms of program specification such as the declarative form (i.e. the desired result is specified, not how to achieve it). Definitions[edit] A programming language is a notation for writing programs, which are specifications of a computation or algorithm.[2] Some, but not all, authors restrict the term "programming language" to those languages that can express all possible algorithms.[2][3] Traits often considered important for what constitutes a programming language include: Function and target Abstractions

Computer science Computer science deals with the theoretical foundations of information and computation, together with practical techniques for the implementation and application of these foundations History[edit] The earliest foundations of what would become computer science predate the invention of the modern digital computer. Blaise Pascal designed and constructed the first working mechanical calculator, Pascal's calculator, in 1642.[3] In 1673 Gottfried Leibniz demonstrated a digital mechanical calculator, called the 'Stepped Reckoner'.[4] He may be considered the first computer scientist and information theorist, for, among other reasons, documenting the binary number system. Although many initially believed it was impossible that computers themselves could actually be a scientific field of study, in the late fifties it gradually became accepted among the greater academic population.[15][16] It is the now well-known IBM brand that formed part of the computer science revolution during this time. Misc

Natural language In the philosophy of language, a natural language (or ordinary language) is any language which arises in an unpremeditated fashion as the result of the innate facility for language possessed by the human intellect. A natural language is typically used for communication, and may be spoken, signed, or written. Natural language is distinguished from constructed languages and formal languages such as computer-programming languages or the "languages" used in the study of formal logic, especially mathematical logic.[1] Defining natural language[edit] Native language learning[edit] The learning of one's own native language, typically that of one's parents, normally occurs spontaneously in early human childhood and is biologically, socially and ecologically driven. Origins of natural language[edit] There is disagreement among anthropologists on when language was first used by humans (or their ancestors). Controlled languages[edit] Constructed languages and international auxiliary languages[edit]

Phrase structure grammar The term phrase structure grammar was originally introduced by Noam Chomsky as the term for grammars as defined by phrase structure rules,[1] i.e. rewrite rules of the type studied previously by Emil Post and Axel Thue (see Post canonical systems). Some authors, however, reserve the term for more restricted grammars in the Chomsky hierarchy: context-sensitive grammars, or context-free grammars. In a broader sense, phrase structure grammars are also known as constituency grammars. Constituency relation[edit] In linguistics, phrase structure grammars are all those grammars that are based on the constituency relation, as opposed to the dependency relation associated with dependency grammars; hence phrase structure grammars are also known as constituency grammars.[2] Any of several related theories for the parsing of natural language qualify as constituency grammars, and most of them have been developed from Chomsky's work, including Dependency relation[edit] Non-descript grammars[edit]

An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities Next: Introduction An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities Andreas Stolcke University of California at Berkeley and International Computer Science Institute Abstract: We describe an extension of Earley's parser for stochastic context-free grammars that computes the following quantities given a stochastic context-free grammar and an input string: a) probabilities of successive prefixes being generated by the grammar; b) probabilities of substrings being generated by the nonterminals, including the entire string being generated by the grammar; c) most likely (Viterbi) parse of the string; d) posterior expected number of applications of each grammar production, as required for reestimating rule probabilities.

Context-Free Grammar Parsing by Message Passing BibTeX @INPROCEEDINGS{Lin93context-freegrammar, author = {Dekang Lin and Randy Goebel}, title = {Context-Free Grammar Parsing by Message Passing}, booktitle = {In proceedings of PACLING 93}, year = {1993}} Bookmark OpenURL Abstract A message passing algorithm for parsing contextfree grammars (CFG) is presented. Bulk File Manager - Home contextual filing Full Feature List Powerful bulk e-mail program that does it all - from the everyday sending and receiving of e-mails to managing reminders, notifications and mailing lists. Mail commander is a super-fast e-mail software with user-friendly interface and extended distribution list capabilities - notifications, reminders, auto-reply, manage returned mail, send mail in batches, etc.. It lets you fire out personalized e-mail shots to existing and prospective customers and is ideal for sending out bulk newsletters and ezines. We offer 4 program options: Mail Commander (Free Edition) Mail Commander Home (Home Edition) Mail Commander Pro (Professional Edition) Mail Commander Deluxe (Deluxe Edition) Program automatically highlights the misspelled words in your message by spelling checker supporting different dictionaries, completes the recipient name you are typing sing information.

Knowledge representation and reasoning Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) devoted to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets. Overview[edit] Knowledge-representation is the field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used to solve complex problems. This hypothesis was not always taken as a given by researchers. History[edit] Characteristics[edit]

Knowledge Engineering Environment On top of KEE several extensions were offered: Simkit,[2][3] a frame-based simulation libraryKEEconnection,[4] database connection between the frame system and relational databases. Frames are called Units in KEE. Units are used for both individual instances and classes. Frames have slots and slots have facets. KEE provides an extensive graphical user interface to create, browse and manipulate frames. KEE also includes a frame-based rule system. KEE supports non-monotonic reasoning through the concepts of worlds. ActiveImages allows graphical displays to be attached to slots of Units. See also[edit] References[edit] External references[edit] An Assessment of Tools for Building Large Knowledge-Based Systems

General Problem Solver While GPS solved simple problems such as the Towers of Hanoi that could be sufficiently formalized, it could not solve any real-world problems because search was easily lost in the combinatorial explosion of intermediate states. The user defined objects and operations that could be done on the objects, and GPS generated heuristics by Means-ends analysis in order to solve problems. It focused on the available operations, finding what inputs were acceptable and what outputs were generated. It then created subgoals to get closer and closer to the goal. References[edit] See also[edit] Web Ontology Language The OWL family contains many species, serializations, syntaxes and specifications with similar names. OWL and OWL2 are used to refer to the 2004 and 2009 specifications, respectively. Full species names will be used, including specification version (for example, OWL2 EL). When referring more generally, OWL Family will be used. History[edit] Early ontology languages[edit] Ontology languages for the web[edit] In 2000 in the United States, DARPA started development of DAML led by James Hendler.[12] In March 2001, the Joint EU/US Committee on Agent Markup Languages decided that DAML should be merged with OIL.[12] The EU/US ad hoc Joint Working Group on Agent Markup Languages was convened to develop DAML+OIL as a web ontology language. OWL started as a research-based[14] revision of DAML+OIL aimed at the semantic web. Semantic web standards[edit] The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.

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