Fast Extraction of Word Embedding from Q-contexts

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.

Accepted by CIKM 2021

References in corpus (5)

• From Frequency to Meaning: Vector Space Models of Semantics
• Convolutional Neural Networks for Sentence Classification
• A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
• Quantum Algorithms for Compositional Natural Language Processing
• Foundations for Near-Term Quantum Natural Language Processing