Türk çocuklarında sözcük sonu demetçik daralması edinimine kısıtlar teorisi yaklaşımı

Bu çalışmada ilk olarak Topbaş ve Kopkalli-Yavuz(2008) tarafından sunulan ana dili Türkçe olan çocuklardaki demetçik daralması edinimi ( acquisition of word-finalconsonant clusters) verileri Kısıtlar Teorisi yaklaşımı çerçevesinde incelenmiştir. Mevcut analizi, sadece [r)+ duraklamalı ünsüz' ses grubunu kapsayan demetçik daralması edinimi veri,leri kullanarak yapılmıştır. Aşamalı Öğrenmek Algoritmasının ( Graddual Learning Algorithm-GLA, Harmonik Dilbilgisi ve Uygunluk Teorisi ( Optimality Theory) yaklaşımlarıyla incelemek ve bu şekilde GLA'nın hangi model altında iyi açıklandığını görmek amaçlanmıştır. Bu yakınsama Praat simülasyon özelliğinin kullanarak simüle edilmiştir ( Boersma & Weenik, 2012, Ana dilde çocuk dili belirli ( marked) olmadığından sadakat ( faithfulness) kısıtlamalarında belirlilik( markadness) kısıtlamalarından daha düşük sıralı değerler atanmıştır (1). Gürültü 2.0'a ayarlamış ve plastisite başlangıçta 0.1'a ayarlanmıştır. Simülasyonların bulguları, Kademeli Öğrenme Algoritmasının, yetişkin hedef kullanımı ile OT-GLA'nın yaptığı gibi yakınsama özelliklerini göstermede Gürültülü HG'ye ( bkz.Tablo 2) daha uyumlu olduğu yönündedir ( bkz. Tablo 3 ). Başka bir deyişle , HG-GLA'nın kazanan/optimal forma ulaşmak için gereken deneme sayısı OT-GLA için gerekli olandan az olduğu görülmüştür.

A constraint-based approach to acquisition of word-final consonant clusters in Turkish children

The current study provides a constraint-based analysis of L1 word-final consonant cluster acquisition in Turkishchild language, based on the data originally presented by Topbas and Kopkalli-Yavuz (2008). The presentanalysis was done using [ɾ]+obstruent consonant cluster acquisition. A comparison of Gradual LearningAlgorithm (GLA) under Optimality Theory (OT) as opposed to Harmonic Grammar (HG) is made to see underwhich model GLA functions more efficiently and reaches the target adult form faster. This convergence wassimulated using the simulation feature of Praat (Boersma & Weenik, 2012). Since child language is unmarked atthe initial state, faithfulness constraints have been assigned lower ranking values than markedness constraints.The noise was set to 2.0 and the plasticity to 0.1. The findings of the simulations show that GLA is morecompatible with Noisy HG in showing convergence properties with the target adult output forms. In other words,the number of trials HG-GLA needed to reach the winning/optimal form was fewer than it was for OT-GLA.

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