Low-dimensional Semantic Space: from Text to Word Embedding
This is an incremental review paper summarizing existing methods for NLP researchers.
The article reviews Word Embedding techniques in NLP, mapping words to low-dimensional vectors, and discusses their application to tasks like word-sense disambiguation and diachronic linguistics, but does not report specific results or numbers.
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.