A Rule-based Computational Model for Gaidhlig Morphology

TL;DR

This paper presents a rule-based computational model for Gaidhlig morphology that leverages limited data from Wiktionary, enhancing interpretability and supporting educational and linguistic tools for this low-resource language.

Contribution

It introduces a declarative rule-based system for Gaidhlig morphology that effectively utilizes limited data and supports derivation of inflected forms using SQL and Python utilities.

Findings

Rule-based model effectively leverages limited data

Supports derivation of inflected forms with SQL and Python

Enhances interpretability and educational utility

Abstract

Language models and software tools are essential to support the continuing vitality of lesser-used languages; however, currently popular neural models require considerable data for training, which normally is not available for such low-resource languages. This paper describes work-in-progress to construct a rule-based model of Gaidhlig morphology using data from Wiktionary, arguing that rule-based systems effectively leverage limited sample data, support greater interpretability, and provide insights useful in the design of teaching materials. The use of SQL for querying the occurrence of different lexical patterns is investigated, and a declarative rule-base is presented that allows Python utilities to derive inflected forms of Gaidhlig words. This functionality could be used to support educational tools that teach or explain language patterns, for example, or to support higher level tools such as rule-based dependency parsers. This approach adds value to the data already present in Wiktionary by adapting it to new use-cases.