Semantic Networks

What Are Sheeple? Apple Users Are In New Merriam-Webster Dictionary Definition

International Business Times

To show the word in action, Merriam-Webster included two example sentences, including one that takes a shot at the folks who prefer Apple's computers and mobile devices over the alternatives. "Apple's debuted a battery case for the juice-sucking iPhone--an ungainly lumpy case the sheeple will happily shell out $99 for," the sentence reads. According to Merriam-Webster's history for the word, sheeple was first used in 1945--more than 30 years before Steve Jobs and Steve Wozniak founded Apple and 62 years before the company would introduce the device that would apparently help herd the sheeple. The case referenced in the sentence is the Smart Battery Case Apple introduced in 2015 for the iPhone 6 and 6s.

GloVe: Global Vectors for Word Representation


Only in the ratio of probabilities does noise from non-discriminative words like water and fashion cancel out, so that large values (much greater than 1) correlate well with properties specific to ice, and small values (much less than 1) correlate well with properties specific of steam. In this way, the ratio of probabilities encodes some crude form of meaning associated with the abstract concept of thermodynamic phase. Owing to the fact that the logarithm of a ratio equals the difference of logarithms, this objective associates (the logarithm of) ratios of co-occurrence probabilities with vector differences in the word vector space. Because these ratios can encode some form of meaning, this information gets encoded as vector differences as well.

Father of artificial intelligence Marvin Minsky dies aged 88


In 1959 he and John McCarthy founded what is now known as the MIT Computer Science and Artificial Intelligence Laboratory. In 1951, Minsky built the first randomly wired neural network learning machine, SNARC. Minsky wrote the book Perceptrons (with Seymour Papert), which became the foundational work in the analysis of artificial neural networks. In the early 1970s, at the MIT Artificial Intelligence Lab, Minsky and Papert developed what came to be called the Society of Mind theory.

Can anybody explain to me what in practice the application could be from vector word representation? • /r/MachineLearning


I am thinking about using this method to train a model for this as my master thesis. But I didn't had anything about it during my classes, I just found the subject and found it interesting. But honestly I don't understand as of yet where you can use this type of models in practice.

WORDNET: A Lexical Database for English


Different relations link the synonym sets. Don't already have an Oxford Academic account? Don't already have an Oxford Academic account? Oxford University Press is a department of the University of Oxford.

TINLAP-2 : Theoretical issues in natural language processing—2


SNePS CONSIDERED AS A FULLY INTENSIONAL PROPOSITIONAL SEMANTIC NETWORK Stuart C. Shapiro and William J. Rapaport Department of Computer Science State University of New York at Buffalo Buffalo, NY 14260 { rapaport 1 Shapiro }%buffalo@csnet-relay ABSTRACT W'e present a formal s\ ntax and semantics for SNePS considered as the (modeled) mind of a cogn:ti\e agent. W'e present a formal syntax and semantics for the SNePS Semantic Network P recessing System (Shapiro 1979), based on a \leinongian theory of the intensional objects of thought (Rapaport 198Sa). Nodes represent the propositions, entities, properties, and relations, while the arcs represent structural links kt \veen these. They include: (1) sensory nodes, which-when SNePS is being used to model a mind-represent interfaces with the external world (in the examples that follow, they represent utterances); (2) base nodes, which represent individual concepts and properties; and (3) variable nodes, which represent arbitrary individuals (Fine 1983) or arbitrary propositions.

A structural paradigm for representing knowledge


Abstract: This report presents on associative network formalism for representing conceptual knowledge. While many similar formalisms have been developed since the introduction of the semantic network in 1966, they have often suffered from inconsistent interpretation of their links, lack of appropriate structure in their nodes, and general expressive inadequacy. In this paper, we take a detailed look at the history of these semantic nets and begin to understand their inadequacies by examining closely what their representational pieces have been intended to model. Based on this analysis, a new type of network is presented - the Structured Inheritance Network (SI-NET) - designed to circumvent common expressive shortcomings.

What's in a concept: Structural foundations for semantic networks


Semantic networks constitute one of the many attempts to capture human knowledge in an abstraction suitable for processing by computer program. We focus here on "concepts"--what net-authors think they are, and how network nodes might represent them. The simplistic view of concept nodes as representing extensional sets is examined, and found wanting in several respects. A level of representation above that of completely uniform nodes and links, but below the level of conceptual knowledge itself, seems to be the key to using previously learned concepts to interpret and structure new ones.

On semantic nets, frames, and associations


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