These lexical mixups shed some light on how LSD affects semantic networks and the way the brain draws connections between different words or concepts.
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EuroWordNet is a system of semantic networks for European languages, based on WordNet.P. Vossen, Ed. 1998. EuroWordNet: A Multilingual Database with Lexical Semantic Networks. Kluwer, Dordrecht, The Netherlands.
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By using the BOINC infrastructure, new semantic networks for the program are built. FreeHAL@home appears to have terminated operations.
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TLC is an instance of a more general class of models known as semantic networks. In a semantic network, each node is to be interpreted as representing a specific concept, word, or feature. That is, each node is a symbol. Semantic networks generally do not employ distributed representations for concepts, as may be found in a neural network.
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Multilayered extended semantic networks (MultiNets) are both a knowledge representation paradigm and a language for meaning representation of natural language expressions that has been developed by Prof. Dr. Hermann Helbig on the basis of earlier Semantic Networks. It is used in a question-answering application for German called InSicht. It is also used to create a tutoring application developed by the university of University of Hagen to teach MultiNet to knowledge engineers.
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RetrievalWare is an enterprise search engine emphasizing natural language processing and semantic networks which was commercially available from 1992 to 2007 and is especially known for its use by government intelligence agencies.
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This is a list of notable individuals who research complex networks, including social networks, biological networks, and semantic networks, among others. Individuals are categorized based on their background and training, or their area of focus.
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Case also recognized that concepts and executive control structures differ across domains in the semantic networks that they involve.Case, R. (1992a). The mind's staircase: Exploring the conceptual underpinnings of children's thought and knowledge. Hillsdale, NJ: Erlbaum.
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RetrievalWare is a relevancy ranking text search system with processing enhancements drawn from the fields of natural language processing (NLP) and semantic networks. NLP algorithms include dictionary-based stemming (also known as lemmatisation) and dictionary-based phrase identification. Semantic networks are used by RetrievalWare to expand the query words entered by the user to related terms with terms weights determined by the distance from the user's original terms. In addition to automatic expansion, a feedback-mode whereby users could choose the meaning of the word before performing the expansion was available.
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In 2004, Leskovec received a Diploma in Computer Science from the University of Ljubljana, Slovenia, researching semantic networks-based creation of abstracts, using machine learning; in 2008 he received a PhD in Computational and Statistical Learning from the Carnegie Mellon University.
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Processing in a semantic network often takes the form of spreading activation (see above). Semantic networks see the most use in models of discourse and logical comprehension, as well as in Artificial Intelligence.Barr, A. & Feigenbaum, E. A. (1982). The handbook of artificial intelligence.
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Lost Altos, CA: William Kaufman. In these models, the nodes correspond to words or word stems and the links represent syntactic relations between them. For an example of a computational implementation of semantic networks in knowledge representation, see Cravo and Martins (1993).
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Unpublished D.Phil. thesis, University of Oxford. found that high schizotypes showed a greater priming effect than controls in such a situation. She argued that this could be accounted for by a relative weakness of inhibitory mechanisms in the semantic networks of high schizotypes.
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He is the co-author with Hector Levesque of a popular book on knowledge representation and reasoning and many scientific papers.Ronald J. Brachman (1983) "What IS-A is and isn't. An Analysis of Taxonomic Links in Semantic Networks"; IEEE Computer, 16 (10); October.
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More dynamic models of semantic networks have been created and tested with neural network experiments based on computational systems such as latent semantic analysis (LSA), Bayesian analysis, and multidimensional factor analysis. The semantics (meaning) of words is studied by all the disciplines of cognitive science.
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Semantic neural network (SNN) is based on John von Neumann's neural network [von Neumann, 1966] and Nikolai Amosov M-Network.Amosov, N. M., A. M. Kasatkin, and L. M. Kasatkina. "Active semantic networks in robots with independent control." Proceedings of the 4th international joint conference on Artificial intelligence-Volume 1.
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Yves A. Lussier is a physician-scientist conducting research in Precision medicine, Translational bioinformatics and Personal Genomics. As a co-founder of Purkinje, he pioneered the commercial use of controlled medical vocabulary organized as directed semantic networks in electronic medical records, as well as Pen computing for clinicians.
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KL-ONE (pronounced "kay ell won") is a well known knowledge representation system in the tradition of semantic networks and frames; that is, it is a frame language. The system is an attempt to overcome semantic indistinctness in semantic network representations and to explicitly represent conceptual information as a structured inheritance network.
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Research has also been applied to enhancing one's social popularity online.Zywica, J., & Danowski, J. (2008). The faces of Facebookers: Investigating social enhancement and social compensation hypotheses; predicting Facebook and offline popularity from sociability and self-esteem, and mapping the meanings of popularity with semantic networks. Journal of Computer-Mediated Communication, 14, 1-34.
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In subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. Some early knowledge graphs were topic-specific. In 1985, Wordnet was founded, capturing semantic relationships between words and meanings an application of this idea to language itself. In 2005, Marc Wirk founded Geonames to capture relationships between different geographic names and locales and associated entities.
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For further use of self-synchronizing circuit technique SNN accepts neurons can be self-running or synchronized. In contrast to the von Neumann network there are no limitations for topology of neurons for semantic networks. It leads to the impossibility of relative addressing of neurons as it was done by von Neumann. In this case an absolute readdressing should be used.
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New York, NY: Plum. or even semantic networks. Both the discrete and continuous classes of stable distribution have properties such as infinitely divisibility, power law tails and unimodality. The most well-known discrete stable distribution is the Poisson distribution which is a special case as the only discrete-stable distribution for which the mean and all higher-order moments are finite.
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The VUE project at Tufts UIT Academic Technology is focused on creating flexible tools for managing and integrating digital resources in support of teaching, learning and research. VUE provides a flexible visual environment for structuring, presenting, and sharing digital information. Using VUE's concept mapping interface, faculty and students design semantic networks of digital resources drawn from digital libraries, local and remote file systems.
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The Bulgarian WordNet (BulNet) is an electronic multilingual dictionary of synonym sets along with their explanatory definitions and sets of semantic relations with other words in the language. It follows the Princeton WordNet (PWN) framework which implements the traditional semantic networks whose structure consists of nodes and relations between the nodes.Koeva, S., G. Totkov and A. Genov. Towards Bulgarian WordNet.
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Collins is most well known in psychology for his foundational research on human semantic memory and cognition. Collins and colleagues, most notably M.R. Quillian and Elizabeth Loftus, developed the position that semantic knowledge is represented in stored category representations, linked together in a taxonomically organized processing hierarchy (see semantic networks). Support for their models came from a classic series of reaction-time experiments on human question answering.
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Every neuron should have a unique identifier that would provide a direct access to another neuron. Of course, neurons interacting by axons-dendrites should have each other's identifiers. An absolute readdressing can be modulated by using neuron specificity as it was realized for biological neural networks. There’s no description for self-reflectiveness and self- modification abilities into the initial description of semantic networks [Dudar Z.V., Shuklin D.E., 2000].
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Research on reasoning in medicine, or clinical reasoning, usually focuses on cognitive processes and/or decision-making outcomes among physicians and patients. Considerations include assessments of risk, patient preferences, and evidence- based medical knowledge. On a cognitive level, clinical inference relies heavily on interplay between abstraction, abduction, deduction, and induction. Intuitive "theories," or knowledge in medicine, can be understood as prototypes in concept spaces, or alternatively, as semantic networks.
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A visual representation of a Semantic Lexicon A semantic lexicon is a digital dictionary of words labeled with semantic classes so associations can be drawn between words that have not previously been encountered. Semantic lexicons are built upon semantic networks, which represent the semantic relations between words. The difference between a semantic lexicon and a semantic network is that a semantic lexicon has definitions for each word, or a "gloss".
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In 1991, he joined Sun Microsystems Laboratories as a Principal Scientist and Distinguished Engineer, and in 2007, he joined ITA Software as a Distinguished Software Engineer. ITA was acquired by Google in 2011, where he now works. Woods' 1975 paper "What's in a Link"William A. Woods, "What's in a Link: Foundations for Semantic Networks". In D. Bobrow and A. Collins (eds.), Representation and Understanding: Studies in Cognitive Science, New York: Academic Press, 1975.
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The defining feature of a semantic network is that its links are almost always directed (that is, they only point in one direction, from a base to a target) and the links come in many different types, each one standing for a particular relationship that can hold between any two nodes.Arbib, M. A. (Ed.). (2002). Semantic networks. In The Handbook of Brain Theory and Neural Networks (2nd ed.), Cambridge, MA: MIT Press.
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Lyons emphasised the distinction between semantic fields and semantic networks. In the 1980s Eva Kittay developed a semantic field theory of metaphor. This approach is based on the idea that the items in a semantic field have specific relations to other items in the same field, and that a metaphor works by re-ordering the relations of a field by mapping them on to the existing relations of another field.Josef Judah Stern, Metaphor in Context, MIT Press, 2000, p242.
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Parker-Rhodes also co-authored papers with Needham on the "theory of clumps" in relation to information retrieval and computational linguistics. He wrote a book on language structure and the logic of descriptions, Inferential Semantics, published in 1978.Inferential Semantics, Humanities Press (1978) The work analyzes sentences and longer passages into mathematical lattices (the kind in Lattice Theory, not crystal lattices) which are semantic networks. These are inferred not only from sentence syntax but also from grammatical focus and sometimes prosody.
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Kurt Bollacker Kurt Bollacker is an American computer scientist with a research background in the areas of machine learning, digital libraries, semantic networks, and electro-cardiographic modeling. He received a Ph.D. in Computer Engineering from The University Of Texas At Austin. Bollacker spent time as a biomedical research engineer at the Duke University Medical Center where worked on electro-cardiography. He is co-creator of the CiteSeer research tool which was produced while he was a visiting researcher at the NEC Research Institute.
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The first semantic networks were built using WordNet. In addition, RetrievalWare implemented a form of n-gram search (branded as APRP - Adaptive Pattern Recognition ProcessingExcalibur Announces Excalibur RetrievalWare 6.5 Featuring RetrievalWare FileRoom - Contains a description of APRP), designed to search over documents with OCR errors. Query terms are divided into sets of 2-grams which are used to locate similarly matching terms from the inverted index. The resulting matches are weighted based on similarly measures and then used to search for documents.
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The slipnet is a network composed of nodes, which represent permanent concepts, and weighted links, which are relations, between them. It differs from traditional semantic networks, since the effective weight associated with a particular link may vary through time according to the activation level of specific concepts (nodes). The codelets build structures in the working area and modify activations in the slipnet accordingly (bottom- up processes), and the current state of slipnet determines probabilistically which codelets must be run (top-down influences).
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For cognitive scientists interested in ontology alignment, the "concepts" are nodes in a semantic network that reside in brains as "conceptual systems." The focal question is: if everyone has unique experiences and thus different semantic networks, then how can we ever understand each other? This question has been addressed by a model called ABSURDIST (Aligning Between Systems Using Relations Derived Inside Systems for Translation). Three major dimensions have been identified for similarity as equations for "internal similarity, external similarity, and mutual inhibition."R.
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Gennady Simeonovich Osipov (October 13, 1948 - 07 July 2020) was a Russian scientist, holding a Ph.D. and a Dr. Sci. in theoretical computer science, information technologies and artificial intelligence. He was the vice- president of the Institute for Systems Analysis of the Russian Academy of Sciences, professor at the Moscow Institute of Physics and Technology (State University), and at Bauman Moscow State Technical University. Osipov has contributed to the Theory of Dynamic Intelligent Systems and heterogeneous semantic networks used in applied intelligent systems.
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Elisabeth Leinfellner studied linguistics at the University of Vienna and University of Munich and worked as a freelance "copy editor." She moved to Lincoln, Nebraska and taught at Doane College in Crete, Nebraska and at the University of Nebraska-Lincoln. in 1975, she cofounded the International Wittgenstein Symposium of the Austrian Ludwig Wittgenstein Society (ALWS). In 1986, she returned to Austria and 1990 she obtained habilitation qualification at the Institute of Linguistics of the University of Vienna with a thesis on semantic networks and context.
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While working in the Russian Ministry of Defense, O. O. Varlamov started developing the theory of “rapid logical inference” in 1985. He was analyzing Petri nets and productions to construct algorithms. Generally, mivar-based theory represents an attempt to combine entity-relationship models and their problem instance – semantic networks and Petri networks. The abbreviation MIVAR was introduced as a technical term by O. O. Varlamov, Doctor of Technical Science, professor at Bauman MSTU in 1993 to designate a “semantic unit” in the process of mathematical modeling.
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The term was coined as early as 1972, in a discussion of how to build modular instructional systems for courses.Edward W. Schneider. 1973. Course Modularization Applied: The Interface System and Its Implications For Sequence Control and Data Analysis. In Association for the Development of Instructional Systems (ADIS), Chicago, Illinois, April 1972 In the late 1980s, Groningen and Twente universities jointly began a project called Knowledge Graphs, focusing on the design of semantic networks with edges restricted to a limited set of relations, to facilitate algebras on the graph.
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"This problem formulation is substantially different to the problem of text understanding for question answering or machine translation. In those reasoning tasks, the vagueness and ambiguity of natural-language expressions can often be kept and translated into other languages. In contrast, robotic agents have to infer missing information pieces and disambiguate the meaning of the instruction in order to perform the instruction successfully." In addition to probabilistic relational models, PRAC uses the principles of analogical reasoning and instance-based learning to infer completions of roles in semantic networks.
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Other groups at BBN were doing original work in cognitive science, instructional research and man-computer communication. Some of the first work on knowledge representation and reasoning (semantic networks), question answering, interactive computer graphics, and computer-aided instruction (CAI) was actively underway. J. C. R. Licklider was the spiritual and scientific leader of much of this work, championing the cause of on-line interaction during an era when almost all computing was being done via batch processing. Wally's initial focus was on expanding the intellectual abilities of extant teaching systems.
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Graph-theoretic methods, in various forms, have proven particularly useful in linguistics, since natural language often lends itself well to discrete structure. Traditionally, syntax and compositional semantics follow tree-based structures, whose expressive power lies in the principle of compositionality, modeled in a hierarchical graph. More contemporary approaches such as head- driven phrase structure grammar model the syntax of natural language using typed feature structures, which are directed acyclic graphs. Within lexical semantics, especially as applied to computers, modeling word meaning is easier when a given word is understood in terms of related words; semantic networks are therefore important in computational linguistics.
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Small-world properties are found in many real-world phenomena, including websites with navigation menus, food webs, electric power grids, metabolite processing networks, networks of brain neurons, voter networks, telephone call graphs, airport networks, and social influence networks. Cultural networks , semantic networks and word co-occurrence networks have also been shown to be small- world networks. Networks of connected proteins have small world properties such as power-law obeying degree distributions. Similarly transcriptional networks, in which the nodes are genes, and they are linked if one gene has an up or down-regulatory genetic influence on the other, have small world network properties.
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Behavioural sciences include two broad categories: neural — Information sciences and social — Relational sciences. Information processing sciences deal with information processing of stimuli from the social environment by cognitive entities in order to engage in decision making, social judgment and social perception for individual functioning and survival of organism in a social environment. These include psychology, cognitive science, behaviour analysis, psychobiology, neural networks, social cognition, social psychology, semantic networks, ethology, and social neuroscience. On the other hand, Relational sciences deal with relationships, interaction, communication networks, associations and relational strategies or dynamics between organisms or cognitive entities in a social system.
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The "association"—a relationship between two pieces of information—is a fundamental concept in psychology, and associations at various levels of mental representation are essential to models of memory and cognition in general. The set of associations among a collection of items in memory is equivalent to the links between nodes in a network, where each node corresponds to a unique item in memory. Indeed, neural networks and semantic networks may be characterized as associative models of cognition. However, associations are often more clearly represented as an N×N matrix, where N is the number of items in memory.
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MultiNet is claimed to be one of the most comprehensive and thoroughly described knowledge representation systems. It specifies conceptual structures by means of about 140 predefined relations and functions, which are systematically characterized and underpinned by a formal axiomatic apparatus. Apart from their relational connections, the concepts are embedded in a multidimensional space of layered attributes and their values. Another characteristic of MultiNet distinguishing it from simple semantic networks is the possibility to encapsulate whole partial networks and represent the resulting conceptual capsule as a node of higher order, which itself can be an argument of relations and functions.
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As content is checked in and out, each use generates new metadata (automatically, to some extent). Information about how (and when) people use the content can allow the system to acquire new filtering, routing and search pathways, corporate taxonomies and semantic networks, and retention-rule decisions. Solutions can provide intranet services to employees (B2E), and can include enterprise portals for business-to-business (B2B), business-to-government (B2G), government-to-business (G2B), or other business relationships. This category includes most former document-management groupware and workflow solutions that had not, by 2016, fully converted their architecture to ECM but provided a web interface.
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The SEMANTICA platform was originally conceived as a method to help biology students learn and retain knowledge about complex organic structures. Joe Faletti, Kathleen Fisher, and several colleagues in the University of California system created SemNet, a computer program used to draw a network of "concepts" connected to each other by "relations". In the late 1960s, Ross Quillian and Allan Collins used the concept of semantic networks as a way of talking about the organization of human semantic memory, or memory for inter-related word concepts. Using SemNet, students could employ simple components to build complex networks.
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After earning her BA from St. John's College in 1977, Forrest studied Computer and Communication Sciences at the University of Michigan, where she received her MS in 1982, and in 1985 her PhD, with a thesis entitled "A study of parallelism in the classifier system and its application to classification in KL-ONE semantic networks." After graduation Forrest worked for Teknowledge Inc. and at the Center for Nonlinear Studies of the Los Alamos National Laboratory. In 1990 she joined the University of New Mexico, where she was appointed Professor of Computer Science and directs the Computer Immune Systems Group, and the Adaptive Computation Laboratory.
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Although she was trained as a linguist, she had deep interests in philosophical questions. Her research interests were both broad and deep including political language, philosophy of language (especially Fritz Mauthner and Ludwig Wittgenstein), semantic networks and cognitive semantics, political and feminist critique of language, rhetoric and argumentation theory, the application of linguistics to the language of literature, and evolutionary mechanisms of the language development. In particular, she focused on Ludwig Wittgenstein and the philosopher and writer Fritz Mauthner. However, she also published on causality and language, on Habermas’ theory of communicative competence from a linguistic point of view and on William Ockham’s and Franz Brentano’s work on semantics.
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The tasks in this area have many potential applications, such as information extraction, question answering, document summarization, machine translation, construction of thesauri and semantic networks, language modeling, paraphrasing, and recognizing textual entailment. In each of these potential applications, the contribution of the types of semantic analysis constitutes the most outstanding research issue. For example, in the word sense induction and disambiguation task, there are three separate phases: #In the training phase, evaluation task participants were asked to use a training dataset to induce the sense inventories for a set of polysemous words. The training dataset consisting of a set of polysemous nouns/verbs and the sentence instances that they occurred in.
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The ultimate goal of the PRAC system is to make knowledge about everyday activities from websites like wikiHow available for service robots, such that they can autonomously acquire new high-level skills by browsing the Web. PRAC addresses the problem that natural language is inherently vague and unspecific. To this end, PRAC maintains probabilistic first-order knowledge bases over semantic networks represented in Markov logic networks. As opposed to other semantic learning initiatives like NELL or IBM's Watson, PRAC does not aim at answering questions in natural language, but to disambiguate and infer information pieces that are missing in natural-language instructions, such that they can be executed by a robot.
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The distinction was originally made by Roger Schank in the mid-1970s to characterize the difference between his work on natural language processing (which represented commonsense knowledge in the form of large amorphous semantic networks) from the work of John McCarthy, Allen Newell, Herbert A. Simon, Robert Kowalski and others whose work was based on logic and formal extensions of logic. Roger Schank actually notes that he originally made this distinction in linguistics, related to Chomskian vs. non-Chomskian, but discovered it works in AI too, and other areas. The distinction was also partly geographical and cultural: "scruffy" was associated with AI research at MIT under Marvin Minsky in the 1960s.
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Grammatical dependency relations are obtained by deep parsing of the text. Hybrid approaches leverage both machine learning and elements from knowledge representation such as ontologies and semantic networks in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so. Open source software tools as well as range of free and paid sentiment analysis tools deploy machine learning, statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.
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Scott Elliott Fahlman (born March 21, 1948) is a computer scientist and Professor Emeritus at Carnegie Mellon University's Language Technologies Institute and Computer Science Department. He is notable for early work on automated planning and scheduling in a blocks world, on semantic networks, on neural networks (especially the cascade correlation algorithm), on the programming languages Dylan, and Common Lisp (especially CMU Common Lisp), and he was one of the founders of Lucid Inc.. During the period when it was standardized, he was recognized as "the leader of Common Lisp." From 2006 to 2015, Fahlman was engaged in developing a knowledge base named Scone, based in part on his thesis work on the NETL Semantic Network.
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In 1993, the widely cited web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber used ontology as a technical term in computer science closely related to earlier idea of semantic networks and taxonomies. Gruber introduced the term as a specification of a conceptualization: > An ontology is a description (like a formal specification of a program) of > the concepts and relationships that can formally exist for an agent or a > community of agents. This definition is consistent with the usage of > ontology as set of concept definitions, but more general. And it is a > different sense of the word than its use in philosophy.
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Corporate taxonomy is the hierarchical classification of entities of interest of an enterprise, organization or administration, used to classify documents, digital assets and other information. Taxonomies can cover virtually any type of physical or conceptual entities (products, processes, knowledge fields, human groups, etc.) at any level of granularity. Corporate taxonomies are increasingly used in information systems (particularly content management and knowledge management systems), as a way to promote discoverability and allow instant access to the right information within exponentially growing volumes of data in learning organizations. Relatively simple systems based on semantic networks and taxonomies proved to be a serious competitor to heavy data mining systems and behavior analysis software in contextual filtering applications used for routing customer requests, "pushing" content on a Web site or delivering product advertising in a targeted and pertinent way.
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Some scholars have claimed that the manuscript's text appears too sophisticated to be a hoax. In 2013 Marcelo Montemurro, a theoretical physicist from the University of Manchester, published findings claiming that semantic networks exist in the text of the manuscript, such as content-bearing words occurring in a clustered pattern, or new words being used when there was a shift in topic. With this evidence, he believes it unlikely that these features were intentionally "incorporated" into the text to make a hoax more realistic, as most of the required academic knowledge of these structures did not exist at the time the Voynich manuscript would have been written. In September 2016, Gordon Rugg and Gavin Taylor addressed these objections in another article in Cryptologia, and illustrated a simple hoax method that they claim could have caused the mathematical properties of the text.
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In later efforts by Todd, a composer would select a set of melodies that define the melody space, position them on a 2-d plane with a mouse-based graphic interface, and train a connectionist network to produce those melodies, and listen to the new "interpolated" melodies that the network generates corresponding to intermediate points in the 2-d plane. More recently a neurodynamical model of semantic networks has been developed to study how the connectivity structure of these networks relates to the richness of the semantic constructs, or ideas, they can generate. It was demonstrated that semantic neural networks that have richer semantic dynamics than those with other connectivity structures may provide insight into the important issue of how the physical structure of the brain determines one of the most profound features of the human mind – its capacity for creative thought.Marupaka, Nagendra, and Ali A. Minai.
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More specifically, it was designed in a manner enabling it to be linked with a rule-based system to form a hybrid system. Although case-based reasoning possesses an advantage over rule-based systems by the elimination of complex semantic networks, it suffers from intractable theoretical obstacles: without some further theory it cannot be predicted what features of a case will turn out to be relevant.Greinke, A. (1994): ‘Legal Expert Systems: A Humanistic Critique of Mechanical Human Inference’ E-Law: Murdoch University Electronic Journal of Law Volume 1, Number 4 (December 1994). Users of SHYSTER therefore require some legal expertise. Richard Susskind argues that “jurisprudence can and ought to supply the models of law and legal reasoning that are required for computerized [sic] implementation in the process of building all expert systems in law.”Susskind, R. (1987): ‘Expert Systems in Law’ (Oxford) p. 20.
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