Deep Natural Language Processing for LinkedIn Search Systems (Research Paper Walkthrough)

Deep Natural Language Processing for LinkedIn Search Systems (Research Paper Walkthrough)

Sep 06, 2021
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#ai #linkedin #nlp Ever wondered How LinkedIn Search system works? This paper from researchers from LinkedIn talks exactly that and tries to answer few interesting questions like: 1. When is deep NLP helpful/not helpful in search systems? 2. How to address latency challenges? 3. How to ensure model robustness? ⏩ Abstract: 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. Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1 ⏩ OUTLINE: 0:00 - Abstract 02:10 - Search Systems at LinkedIn 02:57 - Deep NLP components for Search 04:13 - Overview of a search system 05:56 - Query Intent Prediction 08:32 - Query Tagging 10:44 - Query Auto completion 12:38 - Query Suggestion 15:07 - Document Ranking 18:38 - When is deep nlp helpful? 19:20 - When deep nlp is not helpful? 19:45 - Latency is the biggest challenge in search systems 20:40 - Ensuring robustness and wrap-up ⏩ Paper Title: Deep Natural Language Processing for LinkedIn Search Systems ⏩ Paper: https://arxiv.org/abs/2108.08252 ⏩ Author: Weiwei Guo, Xiaowei Liu, Sida Wang, Michaeel Kazi, Zhoutong Fu, Huiji Gao, Jun Jia, Liang Zhang, Bo Long ⏩ Organisation: LinkedIn ⏩ IMPORTANT LINKS Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu ********************************************** If you want to support me financially which totally optional and voluntary ❤️ You can consider buying me chai ( because i don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizCoffee ********************************************** ⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy ⏩ LinkedIn - https://linkedin.com/in/prakhar21 ⏩ Medium - https://medium.com/@prakhar.mishra ⏩ GitHub - https://github.com/prakhar21 ⏩ Twitter - https://twitter.com/rattller ********************************************* Tools I use for making videos :) ⏩ iPad - https://tinyurl.com/y39p6pwc ⏩ Apple Pencil - https://tinyurl.com/y5rk8txn ⏩ GoodNotes - https://tinyurl.com/y627cfsa #techviz #datascienceguy #nlproc #ai #search #machinelearning

0:00 Abstract 02:10 Search Systems at LinkedIn 02:57 Deep NLP components for Search 04:13 Overview of a search system 05:56 Query Intent Prediction 08:32 Query Tagging 10:44 Query Auto completion 12:38 Query Suggestion 15:07 Document Ranking 18:38 When is deep nlp helpful? 19:20 When deep nlp is not helpful? 19:45 Latency is the biggest challenge in search systems 20:40 Ensuring robustness and wrap-up
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