Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, NLP, and Transformers using TensorFlow by Magnus Ekman

Author:Magnus Ekman [Magnus Ekman]
Language: eng
Format: epub
Publisher: Addison-Wesley Professional
Published: 2021-08-08T16:00:00+00:00

Skip-Gram Model

A skip-gram model is an extension of the n-gram model but where all words do not need to appear sequentially in the training corpus. Instead, some words can be skipped. A k-skip-n-gram model is defined by the two parameters k and n, where k determines how many words can be skipped and n determines how many words each skip-gram contains. For instance, a 1-skip-2-gram model will contain all the bigrams (2-grams) that we discussed previous, but also contain nonconsecutive word pairs that are separated by, at most, one word. If we again consider the word sequence “The more I read, . . .” in addition to /the more/ /more i/, and so on, the 1-skip-2-gram model will contain /the i/ /more read/, and so on.

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