Word Embeddings: A Survey
Computation and Language
2023-05-03 v2 Machine Learning
Machine Learning
Abstract
This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis. These representations are now commonly called word embeddings and, in addition to encoding surprisingly good syntactic and semantic information, have been proven useful as extra features in many downstream NLP tasks.
Cite
@article{arxiv.1901.09069,
title = {Word Embeddings: A Survey},
author = {Felipe Almeida and Geraldo Xexéo},
journal= {arXiv preprint arXiv:1901.09069},
year = {2023}
}
Comments
10 pages, 2 tables, 1 image This version, fixed a typo just before section 3.1