Dot product for similarity in word to vector computation in NLP
The reference points to the word2vec library (source code).
It does not use normalised vectors during training (although it indeed uses the cosine similarity metric for semantic comparisons on already trained vectors).
The reasons for using only dot product instead of cosine similarity during training can be due to:

Dot product is a variation of cosine similarity.
Length captures some semantic information in the sense that length can correlate to frequency of occurance in a given context, so using dot product only captures this information as well (although for strict similarity testing cosine metric is still used)
When vectors are normalised the two metrics coincide.
Efficiency (doing less computations)

References:

Why does the word2vec objective use the inner product (inside the softmax), but the nearest neighbors phase of it uses cosine? It seems like a mismatch.
Should I normalize word2vec's word vectors before using them?
Measuring Word Significance using Distributed Representations of Words
word2vec Parameter Learning Explained