A Survey On Neural Word Embeddings
It synthesizes existing knowledge on neural word embeddings, which have transformed NLP by improving all tasks, but is incremental as a survey.
This survey provides a comprehensive literature review on neural word embeddings, covering theoretical foundations, various types of embeddings, and their impact on NLP tasks, including performance results on benchmark datasets.
Understanding human language has been a sub-challenge on the way of intelligent machines. The study of meaning in natural language processing (NLP) relies on the distributional hypothesis where language elements get meaning from the words that co-occur within contexts. The revolutionary idea of distributed representation for a concept is close to the working of a human mind in that the meaning of a word is spread across several neurons, and a loss of activation will only slightly affect the memory retrieval process. Neural word embeddings transformed the whole field of NLP by introducing substantial improvements in all NLP tasks. In this survey, we provide a comprehensive literature review on neural word embeddings. We give theoretical foundations and describe existing work by an interplay between word embeddings and language modelling. We provide broad coverage on neural word embeddings, including early word embeddings, embeddings targeting specific semantic relations, sense embeddings, morpheme embeddings, and finally, contextual representations. Finally, we describe benchmark datasets in word embeddings' performance evaluation and downstream tasks along with the performance results of/due to word embeddings.