Báo cáo khoa học: "Learning to Rank"

Chia sẻ: Hongphan_1 Hongphan_1 | Ngày: | Loại File: PDF | Số trang:1

Thêm vào BST

Báo xấu

54
lượt xem 2
download

Download Vui lòng tải xuống để xem tài liệu đầy đủ

In this tutorial I will introduce ‘learning to rank’, a machine learning technology on constructing a model for ranking objects using training data. I will ﬁrst explain the problem formulation of learning to rank, and relations between learning to rank and the other learning tasks. I will then describe learning to rank methods developed in recent years, including pointwise, pairwise, and listwise approaches.

Chủ đề:

Bình luận(0) Đăng nhập để gửi bình luận!

Lưu

Nội dung Text: Báo cáo khoa học: "Learning to Rank"

Learning to Rank Hang Li Microsoft Research Asia 4F Sigma Building, No 49 Zhichun Road, Haidian, Beijing China hangli@microsoft.com 1 Introduction 3. Learning to Rank Methods In this tutorial I will introduce ‘learning to rank’, (a) Pointwise Approach a machine learning technology on constructing a i. McRank model for ranking objects using training data. I (b) Pairwise Approach will ﬁrst explain the problem formulation of learn- i. Ranking SVM ing to rank, and relations between learning to ii. RankBoost rank and the other learning tasks. I will then de- iii. RankNet scribe learning to rank methods developed in re- cent years, including pointwise, pairwise, and list- iv. IR SVM wise approaches. I will then give an introduction (c) Listwise Approach to the theoretical work on learning to rank and the i. ListNet applications of learning to rank. Finally, I will ii. ListMLE show some future directions of research on learn- iii. AdaRank ing to rank. The goal of this tutorial is to give the iv. SVM Map audience a comprehensive survey to the technol- v. PermuRank ogy and stimulate more research on the technol- vi. SoftRank ogy and application of the technology to natural (d) Other Methods language processing. Learning to rank has been successfully applied 4. Learning to Rank Theory to information retrieval and is potentially useful (a) Pairwise Approach for natural language processing as well. In fact many NLP tasks can be formalized as ranking i. Generalization Analysis problems and NLP technologies may be signiﬁ- (b) Listwise Approach cantly improved by using learning to rank tech- i. Generalization Analysis niques. These include question answering, sum- ii. Consistency Analysis marization, and machine translation. For exam- ple, in machine translation, given a sentence in the 5. Learning to Rank Applications source language, we are to translate it to a sentence (a) Search Ranking in the target language. Usually there are multi- (b) Collaborative Filtering ple possible translations and it would be better to (c) Key Phrase Extraction sort the possible translations in descending order of their likelihood and output the sorted results. (d) Potential Applications in Natural Lan- Learning to rank can be employed in the task. guage Processing 6. Future Directions for Learning to Rank Re- 2 Outline search 1. Introduction 7. Conclusion 2. Learning to Rank Problem (a) Problem Formulation (b) Evaluation 5 Tutorial Abstracts of ACL-IJCNLP 2009, page 5, Suntec, Singapore, 2 August 2009. c 2009 ACL and AFNLP