LinkedIn open-sources DeText, a framework for natural language processing tasks

LinkedIn today released DeText, an open source framework for natural language process-related ranking, classification, and language generation tasks. It leverages semantic matching, using deep neural networks to understand member intents in search and recommender systems. As a general framework, LinkedIn says it can be applied to a range of tasks, including search and recommendation ranking, multi-class classification, and query understanding. According to LinkedIn senior engineering manager Weiwei Guo, DeText was designed with enough flexibility to meet the requirements of different production services. It's powered by "state-of-the-art" algorithms incorporated in an end-to-end model where the variables are jointly updated, but it attempts to balance its overall effectiveness with high efficiency.