This study applies the Apriori algorithm to analyse patterns, syntactic structures, and thematic clusters in Chinese studies data from various genres. This study aims to identify recurring linguistic elements in order to shed light on the dynamic nature of the Chinese language across different contexts and time periods. The Apriori algorithm is…

Linguistic meaning is generated by the mind and can be expressed in multiple languages. One may assume that equivalent texts/utterances in two languages by means of translation generate equivalent meanings in their readers/hearers. This follows if we assume that meaning calculated from the linguistic input is solely objective in nature. However,…

The digital transformation in educational assessment has led to the proliferation of large-scale data, offering unprecedented opportunities to enhance language learning, and testing through machine learning (ML) techniques. Drawing on the extensive data generated by online English language assessments, this dissertation investigates the efficacy…

Language is one of the hallmarks of intelligence, demanding explanation in a theory of human cognition. However, language presents unique practical challenges for quantitative empirical research, making many linguistic theories difficult to test at naturalistic scales. Artificial neural network language models (LMs) provide a new tool for studying…

This paper describes how a constraint dependency grammar (CDG) parser performs a syntactic analysis of a single sentence (as part of a speech recognition system). The paper extends an existing CDG (developed by H. Maruyama) to process a lattice or graph of sentence hypotheses instead of separate text strings. The paper also describes how the CDG…

Presents a computational study of children's acquisition of word-to-meaning mappings, approximates this task as a mathematical problem, and presents an algorithm for solving the problem, illustrating the algorithm's operation on a small example. Notes that computational simulations demonstrated the robustness of the algorithm and illustrated how…

The application of the "back-propagation" learning algorithm to the task of determining the right set of features corresponding to the words in an input sentence is described. Features that are specific to particular nouns and verbs, that indicate whether a nominal constituent is singular or plural, definite or indefinite, and that…

The grammars of natural languages may be learned by using genetic algorithm systems such as LUST (Linguistics Using Sexual Techniques) that reproduce and mutate grammatical rules and parts-of-speech tags. In document retrieval or filtering systems, applying tags to the list of terms representing a document provides additional information about…

Human behavior shows a variety of serially ordered action sequences. This paper presents a theory of serial order which describes how sequences of actions might be learned and performed. In this theory, parallel interactions across time (coarticulation) and parallel interactions across space (dual-task interference) are viewed as two aspects of a…

Using a system based on standard augmented transition network (ATN) parsing approach, this report describes a technique for the rapid development of natural language parsing, called High-Level Grammar Specification Language (HGSL). The first part of the report describes the syntax and semantics of HGSL and the network implementation of each of its…

Presents an algorithm for use in natural language document retrieval systems which automatically determines if the conjunction "and" in a statement representing an information need should be translated into a Boolean "and" or "or." The results of an experiment that used the algorithm are reported, and further research is suggested. (CLB)