  Vector-based word representations help countless Natural Language Processing
(NLP) tasks capture the language's semantic and syntactic regularities. In this
paper, we present the characteristics of existing word embedding approaches and
analyze them with regard 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. On the other
hand, Neural-network-based approaches 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.
