Artificial Intelligence with Python Cookbook by Unknown

Author:Unknown
Language: eng
Format: epub
Published: 2020-10-29T13:05:56+00:00

w(t) refers to the current word (or concept), while w(t-2), w(t-1), w(t+1), and w(t+2) refer to two words before and after, respectively. We predict the word context based on the current word. As we've already mentioned, the size of the context (window size) is a hyperparameter of the skipgram algorithms.

A related algorithm is the continuous bag-of-words algorithm (CBOW), where the architecture is inverted - we predict a single word based on the context. Both are based on the hypothesis that words that co-occur have related meaning or that they have distributional similarity, which implies that they are similar in terms of their meaning. This is called a distributional hypothesis (Harris, 1954, Distributional structure).

The Walklet algorithm performs well on large graphs and – since it's a neural network – can be trained online. You can find out more about Walklets in the 2017 paper by Brian Perozzi and others, Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings (https://arxiv.org/abs/1605.02115).

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