LEADER 00742nam0-22002411i-450- 001 990001234760403321 035 $a000123476 035 $aFED01000123476 035 $a(Aleph)000123476FED01 035 $a000123476 100 $a--------d--------km-y0itay50------ba 101 0 $aeng 200 1 $a<>Selection of Problems in the Theory of Number.$fby SIERPINSKI W. 210 $aOxford [etc.]$cPergamon Press$d1964. 225 1 $aPopular Lectures in Mathematics$v11 700 1$aSierpinski,$bWaclaw$f<1882-1969>$050582 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001234760403321 952 $a10-I-29$b21243$fMA1 959 $aMA1 996 $aSelection of Problems in the Theory of Number$9382262 997 $aUNINA LEADER 00797nam0-22002891i-450- 001 990002205860403321 005 20021010 035 $a000220586 035 $aFED01000220586 035 $a(Aleph)000220586FED01 035 $a000220586 100 $a20021010d--------km-y0itay50------ba 101 0 $aita 200 1 $aFormulaire vitaminothérapique du praticien$fpar G. Jeanneney et R. de Grailly. 210 $aParis$cG. Doin$d1948. 215 $a202 p.$d21 cm 676 $a 700 1$aJeanneney,$bGeorges$090156 702 1$aGrailly,$bR. De 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002205860403321 952 $a80 XIV D 4$b1501$fFFABC 959 $aFFABC 996 $aFormulaire vitaminothérapique du praticien$9395861 997 $aUNINA DB $aING01 LEADER 03496nam 22006375 450 001 996465981003316 005 20200705163706.0 010 $a3-540-49593-2 024 7 $a10.1007/BFb0021059 035 $a(CKB)1000000000234566 035 $a(SSID)ssj0000321544 035 $a(PQKBManifestationID)11262350 035 $a(PQKBTitleCode)TC0000321544 035 $a(PQKBWorkID)10280351 035 $a(PQKB)11561569 035 $a(DE-He213)978-3-540-49593-2 035 $a(PPN)15516533X 035 $a(EXLCZ)991000000000234566 100 $a20121227d1996 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAutomatic Ambiguity Resolution in Natural Language Processing$b[electronic resource] $eAn Empirical Approach /$fby Alexander Franz 205 $a1st ed. 1996. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1996. 215 $a1 online resource (XX, 164 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v1171 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-62004-4 327 $aPrevious work on syntactic ambiguity resolution -- Loglinear models for ambiguity resolution -- Modeling new words -- Part-of-speech ambiguity -- Prepositional phrase attachment disambiguation -- Conclusions. 330 $aThis is an exciting time for Artificial Intelligence, and for Natural Language Processing in particular. Over the last five years or so, a newly revived spirit has gained prominence that promises to revitalize the whole field: the spirit of empiricism. This book introduces a new approach to the important NLP issue of automatic ambiguity resolution, based on statistical models of text. This approach is compared with previous work and proved to yield higher accuracy for natural language analysis. An effective implementation strategy is also described, which is directly useful for natural language analysis. The book is noteworthy for demonstrating a new empirical approach to NLP; it is essential reading for researchers in natural language processing or computational linguistics. 410 0$aLecture Notes in Artificial Intelligence ;$v1171 606 $aArtificial intelligence 606 $aComputer simulation 606 $aMathematical logic 606 $aStatistics  606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 0$aMathematical logic. 615 0$aStatistics . 615 14$aArtificial Intelligence. 615 24$aSimulation and Modeling. 615 24$aMathematical Logic and Formal Languages. 615 24$aStatistics for Social Sciences, Humanities, Law. 676 $a006.3/5 700 $aFranz$b Alexander$4aut$4http://id.loc.gov/vocabulary/relators/aut$0746051 906 $aBOOK 912 $a996465981003316 996 $aAutomatic Ambiguity Resolution in Natural Language Processing$92830908 997 $aUNISA