Generates a word rank-based probabilistic sampling table.

`make_sampling_table(size, sampling_factor = 1e-05)`

An array of length `size`

where the ith entry is the
probability that a word of rank i should be sampled.

- size
Int, number of possible words to sample.

- sampling_factor
The sampling factor in the word2vec formula.

Used for generating the `sampling_table`

argument for `skipgrams()`

.
`sampling_table[[i]]`

is the probability of sampling the word i-th most common
word in a dataset (more common words should be sampled less frequently, for balance).

The sampling probabilities are generated according to the sampling distribution used in word2vec:

`p(word) = min(1, sqrt(word_frequency / sampling_factor) / (word_frequency / sampling_factor))`

We assume that the word frequencies follow Zipf's law (s=1) to derive a numerical approximation of frequency(rank):

`frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank))`

where `gamma`

is the Euler-Mascheroni constant.

Other text preprocessing:
`pad_sequences()`

,
`skipgrams()`

,
`text_hashing_trick()`

,
`text_one_hot()`

,
`text_to_word_sequence()`