From Exemplar to Grammar: Integrating Analogy and Probability in Language Learning
Rens Bod

Abstract:
We present a new model of language learning which is based on the
following idea: if a language learner does not know which
phrase-structure trees should be assigned to initial sentences, s/he
allows (implicitly) for all possible trees and lets linguistic
experience decide which is the 'best' tree for each sentence. The best
tree is obtained by maximizing 'structural analogy' between a sentence
and previous sentences, which is formalized by the most probable
shortest combination of subtrees from all trees of previous
sentences. Corpus-based experiments with this model on the Penn
Treebank and the Childes database indicate that it can learn both
exemplar-based and rulebased aspects of language, ranging from phrasal
verbs to auxiliary fronting. By having learned the syntactic
structures of sentences, we have also learned the grammar implicit in
these structures, which can in turn be used to produce new
sentences. We show that our model mimicks children's language
development from item-based constructions to abstract constructions,
and that the model can simulate some of the errors made by children in
producing complex questions.