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By contrast, generative theories generally provide performance-based explanations for the oddness of center embedding sentences like one in (2). According to such explanations, the grammar of English could in principle generate such sentences, but doing so in practice is so taxing on working memory that the sentence ends up being unparsable ...
Scrambling is a syntactic phenomenon wherein sentences can be formulated using a variety of different word orders without a substantial change in meaning. Instead the reordering of words, from their canonical position, has consequences on their contribution to the discourse (i.e., the information's "newness" to the conversation).
In transformational grammar, each sentence in a language has two levels of representation: a deep structure and a surface structure. [3] The deep structure represents a sentence's core semantic relations and is mapped onto the surface structure, which follows the sentence's phonological very closely, via transformations.
The generation effect is typically achieved in cognitive psychology experiments by asking participants to generate words from word fragments. [2] This effect has also been demonstrated using a variety of other materials, such as when generating a word after being presented with its antonym, [3] synonym, [1] picture, [4] arithmetic problems, [2] [5] or keyword in a paragraph. [6]
What this means is that for phrase structure rules to be applicable at all, one has to pursue a constituency-based understanding of sentence structure. The constituency relation is a one-to-one-or-more correspondence. For every word in a sentence, there is at least one node in the syntactic structure that corresponds to that word.
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.
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
The sentence appears on a computer monitor word-by-word. After each word, participants were asked to choose if the sentence is still grammatical so far. Then they would go on to rate the sentence from 1 "perfectly good English" to 7 "really bad English." The result showed that ungrammatical sentences were rated to be better than the grammatical ...