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These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus . Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.
Transformer-based paraphrase generation relies on autoencoding, autoregressive, or sequence-to-sequence methods. Autoencoder models predict word replacement candidates with a one-hot distribution over the vocabulary, while autoregressive and seq2seq models generate new text based on the source predicting one word at a time.
A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [1] It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words.
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.
The declarative sentence is the most common kind of sentence in language, in most situations, and in a way can be considered the default function of a sentence. What this means essentially is that when a language modifies a sentence in order to form a question or give a command, the base form will always be the declarative.
Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively.
WordNet aims to cover most everyday words and does not include much domain-specific terminology. WordNet is the most commonly used computational lexicon of English for word-sense disambiguation (WSD), a task aimed at assigning the context-appropriate meanings (i.e. synset members) to words in a text. [ 13 ]
An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.