Probabilistic models of language processing and acquisition

@article{citeulike:1311102, abstract = {Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.}, author = {Chater, Nick and Manning, Christopher D. }, booktitle = {Special issue: Probabilistic models of cognition}, citeulike-article-id = {1311102}, doi = {10.1016/j.tics.2006.05.006}, journal = {Trends in Cognitive Sciences}, keywords = {computational-linguistics, corpus-linguistics, models}, month = {July}, number = {7}, pages = {335--344}, posted-at = {2007-12-18 14:54:31}, priority = {0}, title = {Probabilistic models of language processing and acquisition}, url = {http://dx.doi.org/10.1016/j.tics.2006.05.006}, volume = {10}, year = {2006} }

TY - JOUR ID - citeulike:1311102 L3 - citeulike-article-id:1311102 TI - Probabilistic models of language processing and acquisition T2 - Special issue: Probabilistic models of cognition JF - Trends in Cognitive Sciences VL - 10 IS - 7 SP - 335 EP - 344 N2 - Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception. KW - computational-linguistics KW - corpus-linguistics KW - models AU - Chater, Nick AU - Manning, Christopher PY - 2006/07// UR - http://dx.doi.org/10.1016/j.tics.2006.05.006 ER -