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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.
In linguistics, anglicisation or anglicization is the practice of modifying foreign words, names, and phrases to make them easier to spell, pronounce or understand in English. [1] [2] The term commonly refers to the respelling of foreign words or loan words in English, often to a more drastic degree than that implied in, for example, romanisation.
turning long sentences into multiple shorter ones (or vice versa) expressing abstract concepts more concretely. Paraphrasing with synonyms is considered by some to be an acceptable stage in teaching paraphrase, but it is necessary that it is ultimately combined with techniques for altering sentence structure to avoid the appearance of ...
Phrase structure rules break sentences down into their constituent parts. These constituents are often represented as tree structures (dendrograms). The tree for Chomsky's sentence can be rendered as follows: A constituent is any word or combination of words that is dominated by a single node. Thus each individual word is a constituent.
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.
Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems that can produce understandable texts in English or other human languages from some underlying non-linguistic ...
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