Evaluating Arabic Lexicon Structure with Machine Learning Techniques
Keywords:
Automatic Arabic lexicon generation, web scraping, web information extraction, MLAbstract
In an age where big data reigns supreme, the advent of technological advancements has led to an exponential increase in digital textual data, particularly in the realm of the Arabic language. This surge has spurred the proliferation of electronic Arabic lexicons, which, while abundant, often lack the structured format required for effective use in Natural Language Processing (NLP) applications. This study seeks to bridge this gap by presenting a methodology for the extraction, structuring, and storage of lexicon data to render it suitable for NLP tools and technologies. Utilizing web scraping techniques, the study harvested lexical data from various online sources, transforming it into well-organized Excel files. The corpus encompasses a rich assembly of nouns (10,000 words), verbs (10,000 words), letters (70 words), adverbs (500 words), and pronouns (20 words), thus laying the groundwork for a comprehensive Arabic lexicon. Furthermore, the study leveraged several machine learning models to evaluate the structuring of the lexicon. The Support Vector Machine (SVM) and Random Forest models exhibited commendable accuracy (both at 0.85), underscoring the high quality of the data structuring process. Meanwhile, models like Logistic Regression and Multinomial Naive Bayes, despite lower precision and recall metrics, maintained moderate accuracy, which demonstrates the potential for further refinement.
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