LEADER 05710nam 22006135 450 001 996465863503316 005 20200703044446.0 010 $a3-540-49738-2 024 7 $a10.1007/3-540-60925-3 035 $a(CKB)1000000000234417 035 $a(SSID)ssj0000322264 035 $a(PQKBManifestationID)11247767 035 $a(PQKBTitleCode)TC0000322264 035 $a(PQKBWorkID)10299256 035 $a(PQKB)11588673 035 $a(DE-He213)978-3-540-49738-7 035 $a(PPN)155230514 035 $a(EXLCZ)991000000000234417 100 $a20121227d1996 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aConnectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing$b[electronic resource] /$fedited by Stefan Wermter, Ellen Riloff, Gabriele Scheler 205 $a1st ed. 1996. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1996. 215 $a1 online resource (X, 474 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v1040 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-60925-3 327 $aLearning approaches for natural language processing -- Separating learning and representation -- Natural language grammatical inference: A comparison of recurrent neural networks and machine learning methods -- Extracting rules for grammar recognition from Cascade-2 networks -- Generating English plural determiners from semantic representations: A neural network learning approach -- Knowledge acquisition in concept and document spaces by using self-organizing neural networks -- Using hybrid connectionist learning for speech/language analysis -- SKOPE: A connectionist/symbolic architecture of spoken Korean processing -- Integrating different learning approaches into a multilingual spoken language translation system -- Learning language using genetic algorithms -- A statistical syntactic disambiguation program and what it learns -- Training stochastic grammars on semantical categories -- Learning restricted probabilistic link grammars -- Learning PP attachment from corpus statistics -- A minimum description length approach to grammar inference -- Automatic classification of dialog acts with Semantic Classification Trees and Polygrams -- Sample selection in natural language learning -- Learning information extraction patterns from examples -- Implications of an automatic lexical acquisition system -- Using learned extraction patterns for text classification -- Issues in inductive learning of domain-specific text extraction rules -- Applying machine learning to anaphora resolution -- Embedded machine learning systems for natural language processing: A general framework -- Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique -- Applying an existing machine learning algorithm to text categorization -- Comparative results on using inductive logic programming for corpus-based parser construction -- Learning the past tense of English verbs using inductive logic programming -- A dynamic approach to paradigm-driven analogy -- Can punctuation help learning? -- Using parsed corpora for circumventing parsing -- A symbolic and surgical acquisition of terms through variation -- A revision learner to acquire verb selection rules from human-made rules and examples -- Learning from texts ? A terminological metareasoning perspective. 330 $aThis book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995. Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems. 410 0$aLecture Notes in Artificial Intelligence ;$v1040 606 $aNatural language processing (Computer science) 606 $aArtificial intelligence 606 $aUser interfaces (Computer systems) 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aUser Interfaces and Human Computer Interaction$3https://scigraph.springernature.com/ontologies/product-market-codes/I18067 615 0$aNatural language processing (Computer science). 615 0$aArtificial intelligence. 615 0$aUser interfaces (Computer systems). 615 14$aNatural Language Processing (NLP). 615 24$aArtificial Intelligence. 615 24$aUser Interfaces and Human Computer Interaction. 676 $a006.3/5 702 $aWermter$b Stefan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRiloff$b Ellen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aScheler$b Gabriele$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996465863503316 996 $aConnectionist, statistical and symbolic approaches to learning for natural language processing$91501920 997 $aUNISA