  Many search systems work with large amounts of natural language data, e.g.,
search queries, user profiles and documents, where deep learning based natural
language processing techniques (deep NLP) can be of great help. In this paper,
we introduce a comprehensive study of applying deep NLP techniques to five
representative tasks in search engines. Through the model design and
experiments of the five tasks, readers can find answers to three important
questions: (1) When is deep NLP helpful/not helpful in search systems? (2) How
to address latency challenges? (3) How to ensure model robustness? This work
builds on existing efforts of LinkedIn search, and is tested at scale on a
commercial search engine. We believe our experiences can provide useful
insights for the industry and research communities.
