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Download as PDF; Printable version; In other projects ... Pages in category "Polysemy" The following 8 pages are in this category, out of 8 total. This ...
Polysemy is distinct from monosemy, where a word has a single meaning. [3] Polysemy is distinct from homonymy—or homophony—which is an accidental similarity between two or more words (such as bear the animal, and the verb bear); whereas homonymy is a mere linguistic coincidence, polysemy is not. In discerning whether a given set of meanings ...
The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. [4] It is considered a foundational [5] paper in modern artificial intelligence, as the transformer approach has become the main architecture of large language models like those based on GPT.
The book contains a selection [Note 1] of questions and answers originally published on his blog What If?, along with several new ones. [1] The book is divided into several dozen chapters, most of which are devoted to answering a unique question. [Note 2] What If? was released on September 2, 2014 and was received positively by critics.
AI founder Herbert A. Simon speculated in 1963 that the answers to both these questions was "yes". His evidence was the performance of programs he had co-written, such as Logic Theorist and the General Problem Solver , and his psychological research on human problem solving.
(Reuters) - Reddit is testing an AI-powered feature called Reddit Answers, which scours posts on the social media platform to answer users' queries, it said on Monday. The feature would make it ...
Monosemy as a methodology for analysis is based on the recognition that almost all cases of polysemy (where a word is understood to have multiple meanings) require context in order to differentiate these supposed meanings.
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
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