Libros
Representation Learning for Natural Language Processing
ISBN: 978-981-15-5573-2
Editorial: Springer Nature
Licencia: Creative Commons (by)
Autor(es): Liu, Zhiyuan; [et al.]
Editorial: Springer Nature
Licencia: Creative Commons (by)
Autor(es): Liu, Zhiyuan; [et al.]
In traditional Natural Language Processing (NLP) systems, language entries such as
words and phrases are taken as distinct symbols. Various classic ideas and methods,
such as n-gram and bag-of-words models, were proposed and have been widely
used until now in many industrial applications. All these methods take words as the
minimum units for semantic representation, which are either used to further
estimate the conditional probabilities of next words given previous words
(e.g., n-gram) or used to represent semantic meanings of text (e.g., bag-of-words
models).
[2020]
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