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  2. Neural machine translation - Wikipedia

    en.wikipedia.org/wiki/Neural_machine_translation

    In the translation task, a sentence =, (consisting of tokens ) in the source language is to be translated into a sentence =, (consisting of tokens ) in the target language. The source and target tokens (which in the simple event are used for each other in order for a particular game ] vectors, so they can be processed mathematically.

  3. Artificial grammar learning - Wikipedia

    en.wikipedia.org/wiki/Artificial_grammar_learning

    The AI programs first adapted to simulate both natural and artificial grammar learning used the following basic structure: Given A set of grammatical sentences from some language. Find A procedure for recognizing and/or generating all grammatical sentences in that language. An early model for AI grammar learning is Wolff's SNPR System.

  4. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    After the model is trained, the learned word embeddings are positioned in the vector space such that words that share common contexts in the corpus — that is, words that are semantically and syntactically similar — are located close to one another in the space. [1] More dissimilar words are located farther from one another in the space. [1]

  5. Probabilistic context-free grammar - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_context-free...

    A weighted context-free grammar (WCFG) is a more general category of context-free grammar, where each production has a numeric weight associated with it. The weight of a specific parse tree in a WCFG is the product [ 7 ] (or sum [ 8 ] ) of all rule weights in the tree.

  6. Syntactic parsing (computational linguistics) - Wikipedia

    en.wikipedia.org/wiki/Syntactic_parsing...

    Different theories of grammar propose different formalisms for describing the syntactic structure of sentences. For computational purposes, these formalisms can be grouped under constituency grammars and dependency grammars. Parsers for either class call for different types of algorithms, and approaches to the two problems have taken different ...

  7. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models. BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence ...

  8. Sentence diagram - Wikipedia

    en.wikipedia.org/wiki/Sentence_diagram

    A sentence diagram is a pictorial representation of the grammatical structure of a sentence. The term "sentence diagram" is used more when teaching written language, where sentences are diagrammed. The model shows the relations between words and the nature of sentence structure and can be used as a tool to help recognize which potential ...

  9. Innateness hypothesis - Wikipedia

    en.wikipedia.org/wiki/Innateness_hypothesis

    Language and grammar are only learned through exposure and accumulated experience. This is also called the "nurture" perspective as opposed to the "nature" perspective (linguistic nativism). Chomsky's innateness hypothesis contradicts the belief by John Locke that our knowledge, including language, cannot be innate and is instead derived from ...