# Why would the sampled softmax work? [word2vec]

I'm following Udacity Deep learning course and the instructor briefly explained about the "Sampled Softmax" used in word2vec.

In tensorflow word2vec_basic.py, the implementation is like below.

for j in range(num_skips):
while target in targets_to_avoid:
target = random.randint(0, span - 1) # randomly choose a word from the context of target word
targets_to_avoid.append(target)


I'm having hard time understanding why randomly remove some words would work well even if it reduces the computaion. Can someone shed some light on it?