Vector-based word representations help countless Natural Language Processing
(NLP) tasks capture both semantic and syntactic regularities of the language.
In this paper, we present the characteristics of existing word embedding
approaches and analyze them with regards to many classification tasks. We
categorize the methods into two main groups - Traditional approaches mostly use
matrix factorization to produce word representations, and they are not able to
capture the semantic and syntactic regularities of the language very well.
Neural-Network based approaches, on the other hand, can capture sophisticated
regularities of the language and preserve the word relationships in the
generated word representations. We report experimental results on multiple
classification tasks and highlight the scenarios where one approach performs
better than the rest.

本文通过对现有单词嵌入方法的特点和分类任务的分析，将单词嵌入方法划分为传统方法和基于神经网络的方法，揭示基于神经网络的单词表示方法相比于传统方法更能捕捉语言的语义和句法规律。实验验证了不同方法的性能差异。