From LOJBAN@CUVMB.CC.COLUMBIA.EDU Sat Mar 6 22:47:58 2010 Return-Path: Delivered-To: veion@XIRON.PC.HELSINKI.FI Received: (qmail 4972 invoked from network); 21 May 1997 16:49:54 -0000 Received: from segate.sunet.se (192.36.125.6) by xiron.pc.helsinki.fi with SMTP; 21 May 1997 16:49:54 -0000 Received: from segate.sunet.se by SEGATE.SUNET.SE (LSMTP for OpenVMS v1.1a) with SMTP id <13.392AA6ED@SEGATE.SUNET.SE>; Wed, 21 May 1997 18:49:49 +0100 Date: Wed, 21 May 1997 09:46:31 -0700 Reply-To: jimc@MATH.UCLA.EDU Sender: Lojban list From: Jim Carter Subject: Two articles on language acquisition and grammar X-To: lojban@cuvmb.columbia.edu To: Veijo Vilva Content-Length: 3959 Lines: 72 Message-ID: <6aCkedrFUgD.A.O2G.ew0kLB@chain.digitalkingdom.org> One of the key characteristics of Lojban is that it can be described by an unambiguous grammar which in fact is LALR(1). (Ignoring minor coqmplications at the lex level which are not where the theoretical interest lies.) In two recent articles the authors call into question the relevance of grammar as we know it for language processing by humans, and one of them gives a tantalizing introduction to an alternative formulation of grammar as a problem in optimizing incompatible constraints. Seidenberg, Mark S. Language Acquisition and Use: Learning and Applying Probabilistic Constraints. Science, vol. 275 p. 1599, 1997-03-14. jimc's summary: In the traditional view one knows a language if one knows its grammar, which is a deterministic set of rules for what sentences are allowed in the language. (Semantics is a separate issue which is recognized but not addressed in the Chomskyan model.) Natlang grammar is such a mess that young humans couldn't learn it by listening to adult examples. Therefore (so says Chomsky) many features of grammar must be innate. However, recent work suggests that probabilistic constraints are important, particularly in judging the meaning of an ambiguous phrase. Particularly, a child can tell if a sentence (such as the one he/she is about to speak) is ungrammatical by recognizing that similar or related patterns are rare. Whatever may or may not be innate, there is more than enough input for a child to learn the statistical patterns of the language. Prince, Alan and Paul Smolensky. Optimality: From Neural Networks to Universal Grammar. Science, vol. 275 p. 1604, 1997-03-14. jimc's summary: The linguistic theory of "harmony" holds that rather than generational rules, the kind of grammar important to speakers consists of a set of incompatible rules (such as that the subject is preferred in the first position, and also the shortest argument should be first). The sentences actually produced are the ones most in harmony with the rules. The authors describe how a computerized neural net, which is believed to be an idealized model of biological neural operation, can realize a harmony grammar, while realizing a Chomsky-type grammar in wetware is hard to imagine. In addition, it appears that only a particular subset of harmony grammars is used by humans, and that subset therefore must be innately configured. Specifically, rules are in a strict hierarchy, so that if a stronger rule is satisfied then weaker rules determine the result (which version of a sentence is most in harmony), whereas if the stronger rule is violated, there are no positive credits from satisfying weaker rules. But the importance of particular rules such as word order varies from one language to the next; for example, word order is strong in English, but nearly irrelevant in Latin. More surprising, the nature of the rules is said to be universal among languages, though the importance varies. For example, a prejudice against final consonants is found in all languages, although the prejudice is barely noticeable in English while it is absolute in Chinese. The authors give several examples of rules, but far from a complete list. This theory of optimizing grammar can explain the curious fact that children understand (valid) speech that is much more complex than the sentences they can generate. The theory unifies the procedures of generating and understanding language, in that for generation the "deep structure" to be represented is keyed into the neural net and the "surface structure" floats to the most harmonious value, whereas in understanding, the surface structure is keyed in and the deep structure floats. James F. Carter Voice 310 825 2897 FAX 310 206 6673 UCLA-Mathnet; 6115 MSA; 405 Hilgard Ave.; Los Angeles, CA, USA 90095-1555 Internet: jimc@math.ucla.edu (finger for PGP key) UUCP:...!{ucsd,ames,ncar,gatech,purdue,rutgers,decvax,uunet}!math.ucla.edu!jimc