Towards Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars

Natural Language Processing (NLP) applications often require structured lexical data, yet many traditional dictionaries, such as the Arabic-English Al-Mawrid, are published as flat text designed for human readers rather than machine processing. This paper addresses the challenge of converting the Al-Mawrid dictionary into a machine-readable format. The primary goal is to transform unstructured entries—consisting of streams of words and punctuation—into a hierarchical structure that explicitly labels components like defining phrases, domain labels, and translation equivalences.

To overcome the lack of standardization in Arabic lexicography, the authors propose a modular, cascaded approach. Rather than attempting to parse the entire dictionary in one complex step, they utilize a divide-and-conquer strategy. The process begins with manual data acquisition from the dictionary's CD into a spreadsheet, followed by the creation of a rudimentary database that links headwords to their associated subentries. The core of the structuring process is a parser implemented using Parsing Expression Grammars (PEGs). PEGs were chosen for their ability to handle tokenization and grammar rules within a unified framework, providing the flexibility needed to capture the punctuation patterns observed in the dictionary.

The study confirms that despite the inherent inconsistencies in Arabic dictionary microstructure, it is possible to achieve plausible accuracy in automatic or semi-automatic structuring by focusing on punctuation regularity. By formalizing the dictionary entries, the authors provide a foundation for future NLP tasks, such as machine translation and semantic network construction, which rely on structured lexical knowledge. This work represents the first published attempt to computationally analyze and computerize the Al-Mawrid dictionary, highlighting both the potential for automated lexicography and the specific linguistic challenges—such as ambiguity and inconsistent use of parentheses—that remain for future research.