Format:
1 Online-Ressource (107 Seiten)
Edition:
Also available in print
ISBN:
9781608453405
Series Statement:
Synthesis Lectures on Human Language Technologies #11
Content:
Human language acquisition has been studied for centuries, but using computational modeling for such studies is a relatively recent trend. However, computational approaches to language learning have become increasingly popular, mainly due to advances in developing machine learning techniques, and the availability of vast collections of experimental data on child language learning and child-adult interaction. Many of the existing computational models attempt to study the complex task of learning a language under cognitive plausibility criteria (such as memory and processing limitations that humans face), and to explain the developmental stages observed in children. By simulating the process of child language learning, computational models can show us which linguistic representations are learnable from the input that children have access to, and which mechanisms yield the same patterns of behaviour that children exhibit during this process. In doing so, computational modeling provides insight into the plausible mechanisms involved in human language acquisition, and inspires the development of better language models and techniques. This book provides an overview of the main research questions in the field of human language acquisition. It reviews the most commonly used computational frameworks, methodologies and resources for modeling child language learning, and the evaluation techniques used for assessing these computational models. The book is aimed at cognitive scientists who want to become familiar with the available computational methods for investigating problems related to human language acquisition, as well as computational linguists who are interested in applying their skills to the study of child language acquisition. Different aspects of language learning are discussed in separate chapters, including the acquisition of the individual words, the general regularities which govern word and sentence form, and the associations between form and meaning. For each of these aspects, the challenges of the task are discussed and the relevant empirical findings on children are summarized. Furthermore, the existing computational models that attempt to simulate the task under study are reviewed, and a number of case studies are presented
Content:
2. Computational models of language learning -- What to expect from a model -- Marr's levels of cognitive modeling -- Cognitive plausibility criteria -- Modeling frameworks -- Symbolic modeling -- Connectionist modeling -- Probabilistic modeling -- Research methods -- Available resources -- Analysis of language production data -- Experimental methods of studying language processing -- Summary --
Content:
3. Learning words -- Mapping words to meanings -- Child developmental patterns -- Suggested learning mechanisms -- Existing computational models of word learning -- Case study: associating phonological forms with concepts -- Case study: rule-based cross-situational learning -- Case study: probabilistic cross-situational learning -- Integrating other information resources -- Syntactic structure of the sentence -- Social cues -- Summary --
Content:
4. Putting words together -- Morphology: word form regularities -- Computational models of learning morphology -- Case study: learning English past tense -- Formation of lexical categories -- Computational models of lexical category induction -- Evaluation of the induced categories -- Learning structural knowledge of language -- Nativist accounts of syntax -- Formal studies of learnability -- Case study: models of P & P -- Usage-based accounts of syntax -- Case study: distributional representation of syntactic structure -- Grammar induction from corpora -- Case study: MOSAIC -- Summary --
Content:
5. Form-meaning associations -- Acquisition of verb argument structure -- Semantic bootstrapping -- Construction grammar -- Computational models of construction learning -- Case study: Chang (2004) -- Semantic roles and grammatical functions -- The nature of semantic roles -- Computational studies of semantic roles -- Case study: Alishahi and Stevenson (2010) -- Selectional preferences of verbs -- Computational models of the induction of selectional preferences -- Summary --
Content:
6. Final thoughts -- Standard research methods -- Learning problems -- Bibliography -- Author's biography
Content:
Preface -- 1. Overview -- Language modularity -- Language learnability -- Empirical and computational investigation of linguistic hypotheses -- The scope of this book -- Mapping words to meanings -- Learning syntax -- Linking syntax to semantics --
Note:
Description based upon print version of record
,
Preface; Overview; Language modularity; Language learnability; Empirical and computational investigation of linguistic hypotheses; The scope of this book; Mapping words to meanings; Learning syntax; Linking syntax to semantics; Computational Models of Language Learning; What to expect from a model; Marr's levels of cognitive modeling; Cognitive plausibility criteria; Modeling frameworks; Symbolic modeling; Connectionist modeling; Probabilistic modeling; Research methods; Available resources; Analysis of language production data; Experimental methods of studying language processing; Summary
,
Learning WordsMapping words to meanings; Child developmental patterns; Suggested learning mechanisms; Existing computational models of word learning; Case study: associating phonological forms with concepts; Case study: rule-based cross-situational learning; Case study: probabilistic cross-situational learning; Integrating other information resources; Syntactic structure of the sentence; Social cues; Summary; Putting Words Together; Morphology: word form regularities; Computational models of learning morphology; Case study: learning English past tense; Formation of lexical categories
,
Computational models of lexical category inductionEvaluation of the induced categories; Learning structural knowledge of language; Nativist accounts of syntax; Formal studies of learnability; Case study: models of P & P; Usage-based accounts of syntax; Case study: distributional representation of syntactic structure; Grammar induction from corpora; Case study: MOSAIC; Summary; Form-Meaning Associations; Acquisition of verb argument structure; Semantic bootstrapping; Construction grammar; Computational models of construction learning; Case study: Chang (2004)
,
Semantic roles and grammatical functionsThe nature of semantic roles; Computational studies of semantic roles; Case study: Alishahi and Stevenson (2010); Selectional preferences of verbs; Computational models of the induction of selectional preferences; Summary; Final Thoughts; Standard research methods; Learning problems; Bibliography; Author's Biography;
,
Also available in print.
,
System requirements: Adobe Acrobat Reader.
,
Mode of access: World Wide Web.
Additional Edition:
ISBN 9781608453399
Additional Edition:
Print version Computational Modeling of Human Language Acquisition
Language:
English
Keywords:
Electronic books
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