How LinkedIn Uses Natural Language Processing

The article discusses the completion of member knowledge graphs using Graph Neural Networks (GNNs), specifically introducing a novel model called Entity-BERT.

DeText is an open-source deep NLP framework designed for intelligent text understanding, enhancing search and recommendation systems.

The article discusses how LinkedIn leverages Natural Language Processing (NLP) to enhance user support by predicting the best answers to help requests.

The article discusses the implementation of a scalable comment ranking system on LinkedIn that utilizes machine learning to enhance user engagement by surfacing the most relevant comments.

The article introduces Qingbo Hu, a Senior Business Analytics Associate at LinkedIn, highlighting his background, projects, and interests.

The article discusses the performance issues faced by Java applications running inside Linux Control Groups (cgroups) due to interactions between the Java Virtual Machine (JVM) Garbage Collection (...

The article discusses strategies employed by LinkedIn to reduce the Mean Time to Detect (MTTD) and Mean Time to Recovery (MTTR) for their private cloud management system, Nuage.

The article introduces Ashvin Kannan, VP of Engineering at LinkedIn, highlighting his role in leading the Marketing Solutions engineering team and his previous experience in advertising technology.

The article discusses the technical details of topic mining, a key feature of LinkedIn's Voices platform, which automates the extraction of important concepts from unstructured text data.

The article discusses 'Voices', a text analytics platform developed by LinkedIn to analyze member feedback effectively.

The article discusses Rain, a containerization system developed by LinkedIn to enhance resource allocation and improve application deployment efficiency.

The article introduces LinkedIn Platform as a Service (LPS), a new private cloud solution designed to streamline service deployment and enhance developer productivity.

The article reflects on significant developments in Natural Language Processing (NLP) and highlights key insights from LinkedIn engineers in 2015.

The article discusses LinkedIn's efforts to maintain platform integrity by removing fake accounts created by malicious actors.

The article discusses FeatureFu, an open-source toolkit developed by LinkedIn for feature engineering in machine learning.