Learning to Rank for Information Retrieval and Natural Language Processing


Learning to Rank for Information Retrieval and Natural Language Processing (2011) .. by Hang Li


Table of contents:

Machine generated contents note:

1.Learning to Rank

1.1.Ranking

1.2.Learning to Rank

1.3.Ranking Creation

1.4.Ranking Aggregation

1.5.Learning for Ranking Creation

1.6.Learning for Ranking Aggregation

2.Learning for Ranking Creation

2.1.Document Retrieval as Example

2.2.Learning Task

2.2.1.Training and Testing

2.2.2.Training Data Creation

2.2.3.Feature Construction

2.2.4.Evaluation

2.2.5.Relations with Other Learning Tasks

2.3.Learning Approaches

2.3.1.Pointwise Approach

2.3.2.Pairwise Approach

2.3.3.Listwise Approach

2.3.4.Evaluation Results

3.Learning for Ranking Aggregation

3.1.Learning Task

3.2.Learning Methods

4.Methods of Learning to Rank

4.1.PRank

4.1.1.Model

4.1.2.Learning Algorithm

4.2.OC SVM

4.2.1.Model

4.2.2.Learning Algorithm

4.3.Ranking SVM

4.3.1.Linear Model as Ranking Function

4.3.2.Ranking SVM Model

4.3.3.Learning Algorithm

4.4.IR SVM

4.4.1.Modified Loss Function

4.4.2.Learning Algorithm

4.5.GBRank

4.5.1.Loss Function

4.5.2.Learning Algorithm

4.6.RankNet

4.6.1.Loss Function

4.6.2.Model

4.6.3.Learning Algorithm

4.6.4.Speed up of Training

4.7.Lambda Rank

4.7.1.Loss Function

4.7.2.Learning Algorithm

4.8.ListNet and ListMLE

4.8.1.Plackett-Luce model

4.8.2.ListNet

4.8.3.ListMLE

4.9.AdaRank

4.9.1.Loss Function

4.9.2.Learning Algorithm

4.10.SVM MAP

4.10.1.Loss Function

4.10.2.Learning Algorithms

4.11.SoftRank

4.11.1.Soft NDCG

4.11.2.Approximation of Rank Distribution

4.11.3.Learning Algorithm

4.12.Borda Count

4.13.Markov Chain

4.14.Cranking

4.14.1.Model

4.14.2.Learning Algorithm

4.14.3.Prediction

5.Applications of Learning to Rank

6.Theory of Learning to Rank

6.1.Statistical Learning Formulation

6.2.Loss Functions

6.3.Relations between Loss Functions

6.4.Theoretical Analysis

7.Ongoing and Future Work