Learning to rank for information retrieval and natural language processing

By Hang Li

Subjects: Information retrieval, Ranking and selection (Statistics), Natural language processing (Computer science), Machine learning

Description: Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work.