  Distributed representations of words and paragraphs as semantic embeddings in
high dimensional data are used across a number of Natural Language
Understanding tasks such as retrieval, translation, and classification. In this
work, we propose "Class Vectors" - a framework for learning a vector per class
in the same embedding space as the word and paragraph embeddings. Similarity
between these class vectors and word vectors are used as features to classify a
document to a class. In experiment on several sentiment analysis tasks such as
Yelp reviews and Amazon electronic product reviews, class vectors have shown
better or comparable results in classification while learning very meaningful
class embeddings.
