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008			220601s2015 sz | s |||| 0|eng d
020			_a9783031021558
024	7		_a10.1007/978-3-031-02155-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA278.75 _b2015 EB
100	1		_aLi, Hang, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686959 _d1965-
245	1	0	_aLearning to Rank for Information Retrieval and Natural Language Processing _cby Hang Li
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XIII, 107 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Human Language Technologies _x1947-4059
505	0		_aLearning 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 .
520			_aLearning 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.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9147823 _aRecuperación de la información
650		7	_2embne-DP _9199095 _aEstadística _xProceso de datos
776	0	8	_iPrinted edition: _z9783031010279
776	0	8	_iPrinted edition: _z9783031032837
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02155-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI