Deconstructing word embedding algorithms

Kian Kenyon-Dean, Edward Newell, Jackie Chi Kit Cheung
2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)   unpublished
Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Uncontextualized word embeddings are used in many NLP tasks today, especially in resourcelimited settings where high memory capacity and GPUs are not available. Given the historical success of word embeddings in NLP, we propose a retrospective on some of the most well-known word embedding algorithms. In this work, we deconstruct Word2vec, GloVe, and others, into a
more » ... n form, unveiling some of the common conditions that seem to be required for making performant word embeddings. We believe that the theoretical findings in this paper can provide a basis for more informed development of future models.
doi:10.18653/v1/2020.emnlp-main.681 fatcat:nzzhcxva6rhdtc6hqngxphlk7y