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For example, generative theories generally provide competence-based explanations for why English speakers would judge the sentence in (1) as odd. In these explanations, the sentence would be ungrammatical because the rules of English only generate sentences where demonstratives agree with the grammatical number of their associated noun. [14]
For example, in many variants of transformational grammar, the English active voice sentence "Emma saw Daisy" and its passive counterpart "Daisy was seen by Emma" share a common deep structure generated by phrase structure rules, differing only in that the latter's structure is modified by a passivization transformation rule.
It is also to be expected that the rules will generate syntactically correct but semantically nonsensical sentences, such as the following well-known example: Colorless green ideas sleep furiously This sentence was constructed by Noam Chomsky as an illustration that phrase structure rules are capable of generating syntactically correct but ...
For example, the sentences "Pat loves Chris" and "Chris is loved by Pat" mean roughly the same thing and use similar words. Some linguists, Chomsky in particular, have tried to account for this similarity by positing that these two sentences are distinct surface forms that derive from a common (or very similar [1]) deep structure.
The language equality question (do two given context-free grammars generate the same language?) is undecidable. Context-free grammars arise in linguistics where they are used to describe the structure of sentences and words in a natural language, and they were invented by the linguist Noam Chomsky for this purpose.
From there on, Chomsky tried to build a grammar of Hebrew. Such a grammar would generate the phonetic or sound forms of sentences. To this end, he organized Harris's methods in a different way. [note 18] To describe sentence forms and structures, he came up with a set of recursive rules. These are rules that refer back to themselves.
For example: The man who heard that the dog had been killed on the radio ran away. One can tell if a sentence is center embedded or edge embedded depending on where the brackets are located in the sentence. [Joe believes [Mary thinks [John is handsome.]]] The cat [that the dog [that the man hit] chased] meowed.
Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013. Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words.