Learning to Rank for Information Retrieval and Natural Language Processing por Hang Li

descargar Learning to Rank for Information Retrieval and Natural Language Processing en PDF

Learning to Rank for Information Retrieval and Natural Language Processing Hang Li

AUTOR/AUTORA:

Hang Li

Descripción

Sinopsis

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

Acerca de Hang Li

Hang Li is chief scientist of the Noah’s Ark Lab of Huawei Technologies. He is also adjunct professors of Peking University and Nanjing University. His research areas include information retrieval, natural language processing, statistical machine learning, and data mining. He graduated from Kyoto University in 1988 and earned his PhD from the University of Tokyo in 1998. He worked at the NEC lab in Japan during 1991 and 2001, and Microsoft Research Asia during 2001 and 2012. He joined Huawei Technologies in 2012. Hang has published three technical books and more than 100 scientific papers at top international journals including CL, NLE, TOIS, IPM, IRJ, TWEB, TKDE, and TIST, and top international conferences including SIGIR, WWW, WSDM, ACL, EMNLP, ICML, NIPS, and SIGKDD. He and his colleagues’ papers received the SIGKDD’08 best application paper award, the SIGIR’08 best student paper award, and the ACL’12 best student paper award. Hang worked on the development of several products such as Microsoft SQL Server 2005, Microsoft Office 2007 and Office 2010, Microsoft Live Search 2008, Microsoft Bing 2009, Bing 2010. He has more than 35 granted US patents. Hang has also been very active in the research communities and is serving top international conferences including SIGIR, WWW, WSDM, ACL, EMNLP, NIPS, SIGKDD, and ICDM, and top international journals including CL, TIST, IRJ, JASIST, and JCST.

Datos del libro
Morgan & Claypool Publishers 2014
ISBN: 9781627055857
Idioma: Español
Formatos: pdf epub kindle mobi

En otros lados…

$2376.00

>>> Aquí GRATIS!!! <<<

Información:

  • Veces descargado: 744
  • Tamaño: 641KB
  • Veces leído: 1292

Aquí tienes más Libros de Informática

Llegaste buscando:
Learning to Rank for Information Retrieval and Natural Language Processing descargar epub, Learning to Rank for Information Retrieval and Natural Language Processing gratis sin registrarse, Learning to Rank for Information Retrieval and Natural Language Processing en pdf en español