Editing Computational linguistics (section)

==Modeling language acquisition==
The fact that during [[language acquisition]], children are largely only exposed to positive evidence,<ref>Bowerman, M. (1988). [http://pubman.mpdl.mpg.de/pubman/item/escidoc:468143:4/component/escidoc:532427/bowerman_1988_The-No.pdf The "no negative evidence" problem: How do children avoid constructing an overly general grammar. Explaining language universals].</ref> meaning that the only evidence for what is a correct form is provided, and no evidence for what is not correct,<ref name="autogenerated1971">Braine, M.D.S. (1971). On two types of models of the internalization of grammars. In D.I. Slobin (Ed.), The ontogenesis of grammar: A theoretical perspective. New York: Academic Press.</ref> was a limitation for the models at the time because the now available [[deep learning]] models were not available in late 1980s.<ref name="powers1989">Powers, D.M.W. & Turk, C.C.R. (1989). ''Machine Learning of Natural Language''. Springer-Verlag. {{ISBN|978-0-387-19557-5}}.</ref>

It has been shown that languages can be learned with a combination of simple input presented incrementally as the child develops better memory and longer attention span,<ref name="autogenerated1993">{{cite journal|title= Learning and development in neural networks: The importance of starting small|journal= Cognition|volume= 48|issue= 1|pages= 71–99|doi= 10.1016/0010-0277(93)90058-4|pmid= 8403835|year= 1993|last1= Elman|first1= Jeffrey L.|s2cid= 2105042|citeseerx= 10.1.1.135.4937}}</ref> which explained the long period of [[language acquisition]] in human infants and children.<ref name="autogenerated1993"/>

Robots have been used to test linguistic theories.<ref>{{cite journal | last1 = Salvi | first1 = G. | last2 = Montesano | first2 = L. | last3 = Bernardino | first3 = A. | last4 = Santos-Victor | first4 = J. | year = 2012 | title = Language bootstrapping: learning word meanings from the perception-action association | journal = IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics | volume = 42 | issue = 3| pages = 660–71 | doi = 10.1109/TSMCB.2011.2172420 | pmid = 22106152 | arxiv = 1711.09714 | s2cid = 977486 }}</ref> Enabled to learn as children might, models were created based on an [[affordance]] model in which mappings between actions, perceptions, and effects were created and linked to spoken words. Crucially, these robots were able to acquire functioning word-to-meaning mappings without needing grammatical structure.

Using the [[Price equation]] and [[Pólya urn]] dynamics, researchers have created a system which not only predicts future linguistic evolution but also gives insight into the evolutionary history of modern-day languages.<ref>{{cite journal|author1=Gong, T.|author2=Shuai, L.|author3=Tamariz, M.|author4=Jäger, G.|name-list-style=amp|year=2012|title=Studying Language Change Using Price Equation and Pólya-urn Dynamics|editor=E. Scalas|journal=PLOS ONE|volume=7|issue=3|page=e33171|doi=10.1371/journal.pone.0033171|pmid=22427981|pmc=3299756|bibcode=2012PLoSO...733171G|doi-access=free}}</ref>