Automating rule generation for grammar checkers

TL;DR

This paper explores automatic and semi-automatic methods for generating symbolic rules for grammar checkers using corpora and machine learning, aiming to enhance rule-based systems efficiently.

Contribution

It introduces new approaches combining machine learning with corpus analysis to automate rule creation for grammar checkers, reducing manual effort.

Findings

Symbolic machine learning algorithms can effectively acquire new grammar rules.

Error corpora alone are insufficient for comprehensive grammar checking.

Combining multiple approaches improves coverage and reduces false alarms.

Abstract

In this paper, I describe several approaches to automatic or semi-automatic development of symbolic rules for grammar checkers from the information contained in corpora. The rules obtained this way are an important addition to manually-created rules that seem to dominate in rule-based checkers. However, the manual process of creation of rules is costly, time-consuming and error-prone. It seems therefore advisable to use machine-learning algorithms to create the rules automatically or semi-automatically. The results obtained seem to corroborate my initial hypothesis that symbolic machine learning algorithms can be useful for acquiring new rules for grammar checking. It turns out, however, that for practical uses, error corpora cannot be the sole source of information used in grammar checking. I suggest therefore that only by using different approaches, grammar-checkers, or more generally, computer-aided proofreading tools, will be able to cover most frequent and severe mistakes and avoid false alarms that seem to distract users.