{"id":151,"date":"2008-09-15T05:00:24","date_gmt":"2008-09-15T09:00:24","guid":{"rendered":"http:\/\/whamit.mit.edu\/?p=151"},"modified":"2008-09-15T16:14:40","modified_gmt":"2008-09-15T20:14:40","slug":"mit-linguistics-colloquium-jason-riggle-sept-19th","status":"publish","type":"post","link":"https:\/\/ling-phil.mit.edu\/whamit\/2008\/09\/15\/mit-linguistics-colloquium-jason-riggle-sept-19th\/","title":{"rendered":"MIT Linguistics Colloquium - Jason Riggle - Sept. 19th"},"content":{"rendered":"<p>Friday, Sept. 19, 3:30 PM<BR><br clear='none'\/>\n<A HREF=\"http:\/\/whereis.mit.edu\/map-jpg?mapterms=32-141&mapsearch=go\">32D-141<\/A><BR><\/p>\n<p><A HREF=\"http:\/\/hum.uchicago.edu\/~jriggle\/\">Jason Riggle<\/A><BR><br clear='none'\/>\nUniversity of Chicago<\/p>\n<p>&#8220;Learning and Linguistic Typology&#8221;<\/p>\n<p>>In this talk, I present results on the learnability of phonological grammars for two constraint-based models,<br clear='none'\/>\nHarmonic Grammar (HG; Legendre, Miyata, and Smolensky 1990) and Optimality Theory (OT; Prince<br clear='none'\/>\nand Smolensky 1993). I first establish that grammars in these models are learnable from reasonably sized<br clear='none'\/>\nsamples of data and then present a learning algorithm for OT that is guaranteed to make no more than<br clear='none'\/>\nk log2 k mistakes when learning grammars with k constraints, which is only logarithmically worse than the<br clear='none'\/>\nbest possible learning algorithm for OT. <\/p>\n<p>>The proposed learning algorithm calculates the number of rankings that are consistent with a set of data.<br clear='none'\/>\nThis makes possible a simple and effective Bayesian heuristic for learning \u0096 all else equal, choose can-<br clear='none'\/>\ndidates that are preferred by the highest number of rankings consistent with previous observations. This<br clear='none'\/>\ngeneral strategy can be applied to HG or to any parameterized model of grammar, and it associates with<br clear='none'\/>\neach language generated by the theory an abstract quantity measuring the fraction of the parameter space<br clear='none'\/>\ncorresponding to grammars that generate that language. <\/p>\n<p>>The p-volume seems to encode \u0091restrictiveness\u0092 in a way similar to Tesar and Prince\u0092s (1999) r-measure.<br clear='none'\/>\nPreliminary investigations indicate that p-volume is significantly correlated with typological frequency<br clear='none'\/>\n(cf. Bane and Riggle 2008). This fact is neatly explained if language learners use a strategy that is some-<br clear='none'\/>\ntimes called a Gibbs leaner wherein they keep track of the region of the parameter space consistent with<br clear='none'\/>\nprevious observations but make guesses according to a single hypothesis grammar randomly selected from<br clear='none'\/>\nthat region. Upon making an error the Gibbs leaner updates the parameter region and randomly selects a<br clear='none'\/>\nnew hypothesis grammar from that region. Following this strategy, learners will be predisposed towards<br clear='none'\/>\ngrammars with large p-volume in cases where the hypotheses are underdetermined by the data. Moreover,<br clear='none'\/>\npriors other than the \u0091flat\u0092 distribution over rankings can be included to implement models of ranking bias. <\/p>\n<p>>One of the primary assets of this strategy is that it allows linguistic theory to be informed by the relative<br clear='none'\/>\nfrequencies of patterns in linguistic typologies rather than only by the boolean distinction of whether or not<br clear='none'\/>\na pattern is attested. Though some of the frequency asymmetries surely come from non-linguistic historical<br clear='none'\/>\naccidents, a model of learning that is able to account for some of the frequency variance is clearly of interest<br clear='none'\/>\nand makes a range of predictions that can be tested in experimental settings. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Friday, Sept. 19, 3:30 PM 32D-141 Jason Riggle University of Chicago &#8220;Learning and Linguistic Typology&#8221; >In this talk, I present results on the learnability of phonological grammars for two constraint-based models, Harmonic Grammar (HG; Legendre, Miyata, and Smolensky 1990) and Optimality Theory (OT; Prince and Smolensky 1993). I first establish that grammars in these models [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-151","post","type-post","status-publish","format-standard","hentry","category-talks"],"_links":{"self":[{"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/posts\/151","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/comments?post=151"}],"version-history":[{"count":1,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/posts\/151\/revisions"}],"predecessor-version":[{"id":152,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/posts\/151\/revisions\/152"}],"wp:attachment":[{"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/media?parent=151"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/categories?post=151"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/tags?post=151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}