Deep Natural Language Processing for LinkedIn Search Systems

Deep Natural Language Processing for LinkedIn Search Systems

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...

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Hauptverfasser: Guo, Weiwei, Liu, Xiaowei, Wang, Sida, Kazi, Michaeel, Fu, Zhoutong, Gao, Huiji, Jia, Jun, Zhang, Liang, Long, Bo
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Computer Science - Computation and Language
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creator Guo, Weiwei
Liu, Xiaowei
Wang, Sida
Kazi, Michaeel
Fu, Zhoutong
Gao, Huiji
Jia, Jun
Zhang, Liang
Long, Bo
description 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.
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Computer Science - Computation and Language
title Deep Natural Language Processing for LinkedIn Search Systems
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