Representation Learning and NLP

Abstract

Natural language processing (NLP) aims to build linguistic-specific programs for machines to understand and use human languages. Conventional NLP methods heavily rely on feature engineering to constitute semantic representations of text, requiring careful design and considerable expertise. Meanwhile, represen-tation learning aims to automatically build informative representations of raw data for further application and achieves significant success in recent years. This chapter presents a brief introduction to representation learning, including its motivation, history, intellectual origins, and recent advances in both machine learning and NLP.