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Many search systems work with large amounts of natural language data, e.g.,search queries, user profiles, and documents. Building a successful searchsystem requires a thorough understanding of textual data semantics, where deeplearning based natural language processing techniques (deep NLP) can be ofgreat help. In this paper, we introduce a comprehensive study for applying deepNLP techniques to five representative tasks in search systems: query intentprediction (classification), query tagging (sequential tagging), documentranking (ranking), query auto completion (language modeling), and querysuggestion (sequence to sequence). We also introduce BERT pre-training as asixth task that can be applied to many of the other tasks. Through the modeldesign and experiments of the six tasks, readers can find answers to fourimportant questions: (1). When is deep NLP helpful/not helpful in searchsystems? (2). How to address latency challenges? (3). How to ensure modelrobustness? This work builds on existing efforts of LinkedIn search, and istested at scale on LinkedIn's commercial search engines. We believe ourexperiences can provide useful insights for the industry and researchcommunities.

Deep Natural Language Processing for LinkedIn Search