A Rule-Induction Approach for Building an Arabic Language Interfaces to Databases

Abstract

In the field of Natural Language Interfaces for Databases (NLIDB), most of the solutions considered for translating natural language queries into database query language is based on linguistic operations. The application of these operations makes it possible to translate the natural language queries into an unambiguous logical interpretation. However, this task is extremely complex and requires excessive time. While nowadays emphasis is placed on the use of machine learning approaches to automate the operation of natural language processing systems. From this, the automation of the natural language queries translation process into a logical interpretation is interesting and remains a major challenge in the field NLIDB. Also, it can have a direct impact on reducing the complexity of the operation of NLIDB. In this study, we focused on applying a new approach to automate the operation of NLIDB. In this approach, we applied a supervised learning technique to induce rules that transform natural language queries into unambiguous expressions.

On Ambiguity in the Arabic Language: Scrutinizing Translation Issues through Machine Translation from English and French into Arabic

“…These theories elucidate how phonological and morphological components within linguistic structures interact within a grammatical framework (McCarthy & Prince, 1996). Bais and Machkour (2023) endorses the prosodic morphology framework as optimal for analyzing Arabic diminutive formation, as it addresses the nonconcatenative templatic morphology of the language.…”

“…This approach couples an Arabic-French statistical MT system using the Moses decoder with additional morphological rules to simplify the source language morphology. Another advancement includes creating Arabic language interfaces for databases, applying supervised learning to transform natural language queries into unambiguous expressions (Bais & Machkour, 2023).…”