  The notion of word embedding plays a fundamental role in natural language
processing (NLP). However, pre-training word embedding for very large-scale
vocabulary is computationally challenging for most existing methods. In this
work, we show that with merely a small fraction of contexts (Q-contexts)which
are typical in the whole corpus (and their mutual information with words), one
can construct high-quality word embedding with negligible errors. Mutual
information between contexts and words can be encoded canonically as a sampling
state, thus, Q-contexts can be fast constructed. Furthermore, we present an
efficient and effective WEQ method, which is capable of extracting word
embedding directly from these typical contexts. In practical scenarios, our
algorithm runs 11$\sim$13 times faster than well-established methods. By
comparing with well-known methods such as matrix factorization, word2vec,
GloVeand fasttext, we demonstrate that our method achieves comparable
performance on a variety of downstream NLP tasks, and in the meanwhile
maintains run-time and resource advantages over all these baselines.
