Fast Extraction of Word Embedding from Q-contexts

TL;DR

This paper introduces a fast and efficient method for extracting high-quality word embeddings from a small subset of contexts, significantly reducing computation time while maintaining competitive performance.

Contribution

The authors propose the WEQ method that constructs word embeddings from Q-contexts, enabling rapid embedding extraction with minimal loss in quality.

Findings

Runs 11-13 times faster than traditional methods

Achieves comparable NLP task performance to established methods

Maintains resource efficiency and scalability

Abstract

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.