In this technical blog post, learn how LinkedIn uses deep learning-based NLP technologies to better understand text semantics, which is key to any ranking model.
In this technical blog post, learn how LinkedIn uses deep learning-based NLP technologies to better understand text semantics, which is key to any ranking model.
Speech and natural language processing (NLP) have become the foundation for most of the AI development in the enterprise today, as textual data represents a significant portion of unstructured content. As consumer internet companies continue to improve the accuracy of conversational AI, search, and recommendation systems, there is an increasing need for processing rich text data efficiently and effectively.
However, one of the key challenges for achieving the desired accuracy lies in understanding complex semantics and underlying user intent, and effectively extracting relevant information from a variety of sources such as user queries and documents. In recent years, the rapid development of deep learning models has bolstered improvements for a variety of NLP tasks, indicating the vast potential for further improving the accuracy of search and recommender systems.
In this post, we introduce DeText, a state-of-the-art, open-source NLP framework developed at LinkedIn for text understanding, followed by a summary of the GPU-accelerated BERT assets available for you to jumpstart your NLP development.