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Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K. and Zettlemoyer, L. (2018) Deep Contextualized Word Representations. In: Walker, M., Ji, H. and Stent, A., Eds., Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), Association for Computational Linguistics, New Orleans, 2227-2237.
https://doi.org/10.18653/v1/N18-1202

has been cited by the following article:

• TITLE: Word Embeddings and Semantic Spaces in Natural Language Processing

  AUTHORS: Peter J. Worth

  KEYWORDS: Natural Language Processing, Vector Space Models, Semantic Spaces, Word Embeddings, Representation Learning, Text Vectorization, Machine Learning, Deep Learning

  JOURNAL NAME: International Journal of Intelligence Science, Vol.13 No.1, January 17, 2023

  ABSTRACT: One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.