Learning to Rank for Information Retrieval and Natural Language Processing

Description

Learning to rank refers to machine learning techniques for training a 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 its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work Preface Learning to Rank Ranking Learning to Rank Ranking Creation Ranking Aggregation Learning for Ranking Creation Learning for Ranking Aggregation Learning for Ranking Creation Document Retrieval as Example Learning Task Training and Testing Training Data Creation Feature Construction Evaluation Relations with Other Learning Tasks Learning Approaches Pointwise Approach Pairwise Approach Listwise Approach Evaluation Results Learning for Ranking Aggregation Learning Task Learning Methods Methods of Learning to Rank PRank Model Learning Algorithm OC SVM Model Learning Algorithm McRank Model Learning Algorithm Ranking SVM Linear Model as Ranking Function Ranking SVM Model Learning Algorithm IR SVM Modified Loss Function Learning Algorithm GBRank Loss Function Learning Algorithm RankNet Loss Function Model Learning Algorithm Speed up of Training ListNet and ListMLE Plackett-Luce model ListNet ListMLE AdaRank Loss Function Learning Algorithm SVM MAP Loss Function Learning Algorithms SoftRank Soft NDCG Approximation of Rank Distribution Learning Algorithm LambdaRank Loss Function Learning Algorithm LambdaMART Model and Loss Function Learning Algorithm Borda Count Markov Chain Cranking Model Learning Algorithm Prediction Applications of Learning to Rank Theory of Learning to Rank Statistical Learning Formulation Loss Functions Relations between Loss Functions Theoretical Analysis Ongoing and Future Work Bibliography Author's Biography