000 04609nam a22005175i 4500
001 978-3-031-02141-1
003 DE-He213
005 20240730163554.0
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008 221028s2011 sz | s |||| 0|eng d
020 _a9783031021411
_9978-3-031-02141-1
024 7 _a10.1007/978-3-031-02141-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLi, Hang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979298
245 1 0 _aLearning to Rank for Information Retrieval and Natural Language Processing
_h[electronic resource] /
_cby Hang Li.
250 _a1st ed. 2011.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2011.
300 _aIV, 113 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aIntroduction -- 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 the 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 the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing 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 SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, 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: Introduction / 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
710 2 _aSpringerLink (Online service)
_979299
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010132
776 0 8 _iPrinted edition:
_z9783031032691
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_979300
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02141-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c84753
_d84753