An Inference-rules based Categorial Grammar Learner for Simulating Language Acquisition
نویسندگان
چکیده
We propose an unsupervised inference rules-based categorial grammar learning method, which aims to simulate language acquisition. The learner has been trained and tested on an artificial language fragment that contains both ambiguity and recursion. We demonstrate that the learner has 100% coverage with respect to the target grammar using a relatively small set of initial assumptions. We also show that our method is successful at two of the celebrated problems of language acquisition literature: learning English auxiliary fronting in polar interrogatives and English auxiliary word order.
منابع مشابه
Unsupervised Syntax Learning with Categorial Grammars Using Inference Rules
We propose a learning method with categorial grammars using inference rules. The proposed learning method has been tested on an artificial language fragment that contains both ambiguity and recursion. We demonstrate that our learner has successfully converged to the target grammar using a relatively small set of initial assumptions. We also show that our method is successful at one of the celeb...
متن کاملThe acquisition of a unification-based generalised categorial grammar
The purpose of this work is to investigate the process of grammatical acquisition from data. In order to do that, a computational learning system is used, composed of a Universal Grammar with associated parameters, and a learning algorithm, following the Principles and Parameters Theory. The Universal Grammar is implemented as a Unification-Based Generalised Categorial Grammar, embedded in a de...
متن کاملComputational models for first language acquisition
This work investigates a computational model of first language acquisition; the Categorial Grammar Learner or CGL. The model builds on the work of Villavicenio, who created a parametric Categorial Grammar learner that organises its parameters into an inheritance hierarchy, and also on the work of Buszkowski and Kanazawa, who demonstrated the learnability of a k-valued Classic Categorial Grammar...
متن کاملA Quantitative Evaluation Of Naturalistic Models Of Language Acquisition; The Efficiency Of The Triggering Learning Algorithm Compared To A Categorial Grammar Learner
Naturalistic theories of language acquisition assume learners to be endowed with some innate language knowledge. The purpose of this innate knowledge is to facilitate language acquisition by constraining a learner’s hypothesis space. This paper discusses a naturalistic learning system (a Categorial Grammar Learner (CGL)) that differs from previous learners (such as the Triggering Learning Algor...
متن کاملModeling Acquisition of Word Structure with Lexicalized Grammar Learning
Introduction This paper introduces a framework for learning structure in natural languages, and reports results from a simple application of it to learning word-syntax of an agglutinative language in an unsupervised manner. Arguably, the learning environment of children acquiring languages provides more information—by means of linguistic interaction and extralinguistic information present in th...
متن کامل