LEADER 03903nam 2200733 450 001 9910461722303321 005 20200520144314.0 010 $a0-262-32681-7 010 $a0-262-32680-9 035 $a(CKB)3710000000473163 035 $a(EBL)4093098 035 $a(SSID)ssj0001552362 035 $a(PQKBManifestationID)16171417 035 $a(PQKBTitleCode)TC0001552362 035 $a(PQKBWorkID)12727616 035 $a(PQKB)10460272 035 $a(StDuBDS)EDZ0001375567 035 $a(MiAaPQ)EBC4093098 035 $a(OCoLC)921143252 035 $a(MdBmJHUP)muse49203 035 $a(OCoLC)921143252$z(OCoLC)990531596 035 $a(OCoLC-P)921143252 035 $a(MaCbMITP)10008 035 $a(Au-PeEL)EBL4093098 035 $a(CaPaEBR)ebr11119530 035 $a(CaONFJC)MIL829506 035 $a(EXLCZ)993710000000473163 100 $a20151201h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProductivity and reuse in language $ea theory of linguistic computation and storage /$fTimothy J. O'Donnell 210 1$aCambridge, Massachusetts ;$aLondon, England :$cThe MIT Press,$d2015. 210 4$dİ2015 215 $a1 online resource (350 p.) 300 $aDescription based upon print version of record. 311 $a0-262-02884-0 320 $aIncludes bibliographical references and index. 327 $aContents; Preface; Acknowledgments; I MODEL BACKGROUND AND DEVELOPMENT; 1 Introduction; 2 The Framework; 3 Formalization of the Models and Inference; II EMPIRICAL APPLICATIONS; 4 The English Past Tense: Abstraction and Competition; 5 The English Past Tense: Simulations; 6 English Derivational Morphology: Productivity, Processing, and Ordering; 7 English Derivational Morphology: Simulations; 8 Conclusion; A Past-Tense Inflectional Classes; B Derivational Suffixes; Bibliography; Index 330 $a"Language allows us to express and comprehend an unbounded number of thoughts. This fundamental and much-celebrated property is made possible by a division of labor between a large inventory of stored items (e.g., affixes, words, idioms) and a computational system that productively combines these stored units on the fly to create a potentially unlimited array of new expressions. A language learner must discover a language's productive, reusable units and determine which computational processes can give rise to new expressions. But how does the learner differentiate between the reusable, generalizable units (for example, the affix -ness, as in coolness, orderliness, cheapness) and apparent units that do not actually generalize in practice (for example, -th, as in warmth but not coolth)? In this book, Timothy O'Donnell proposes a formal computational model, Fragment Grammars, to answer these questions. This model treats productivity and reuse as the target of inference in a probabilistic framework, asking how an optimal agent can make use of the distribution of forms in the linguistic input to learn the distribution of productive word-formation processes and reusable units in a given language"--MIT CogNet. 606 $aPsycholinguistics$xMathematical models 606 $aMemory 606 $aLanguage and languages 606 $aCognitive grammar 606 $aRecognition 606 $aPsycholinguistics 608 $aElectronic books. 615 0$aPsycholinguistics$xMathematical models. 615 0$aMemory. 615 0$aLanguage and languages. 615 0$aCognitive grammar. 615 0$aRecognition. 615 0$aPsycholinguistics. 676 $a410.1/51 700 $aO'Donnell$b Timothy J.$f1977-$01057188 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910461722303321 996 $aProductivity and reuse in language$92492188 997 $aUNINA