  This article focuses on the study of Word Embedding, a feature-learning
technique in Natural Language Processing that maps words or phrases to
low-dimensional vectors. Beginning with the linguistic theories concerning
contextual similarities - "Distributional Hypothesis" and "Context of
Situation", this article introduces two ways of numerical representation of
text: One-hot and Distributed Representation. In addition, this article
presents statistical-based Language Models(such as Co-occurrence Matrix and
Singular Value Decomposition) as well as Neural Network Language Models (NNLM,
such as Continuous Bag-of-Words and Skip-Gram). This article also analyzes how
Word Embedding can be applied to the study of word-sense disambiguation and
diachronic linguistics.
