- The paper proposes a supervised machine learning framework to classify Arabic text readability into four levels (Easy, Medium, Difficult, Very Difficult) for language learners.
- A large corpus of 39,792 Arabic documents was manually categorized, preprocessed (tokenizing, filtering, normalizing, stemming), and used with statistical features (Count, TF-IDF) for classification.
- Experimental evaluation showed high accuracy, with SVM and Multinomial Naive Bayes performing best using n-gram features (unigrams and bigrams combined) for predicting text difficulty levels.
The paper "Efficient Measuring of Readability to Improve Documents Accessibility for Arabic Language Learners" presents a novel approach to predict text complexity levels specifically tailored for Arabic language learners. The approach leverages supervised machine learning techniques to construct a classifier capable of discriminating between varying levels of text difficulty. This method intends to facilitate the selection of suitable reading materials for learners, thus enhancing educational outcomes.
Data Collection and Preprocessing
A substantial corpus of 39,792 Arabic documents was amassed from various online sources. These documents were manually categorized into four distinct complexity levels: Easy, Medium, Difficult, and Very Difficult. These classifications were informed by specific target audience criteria, such as primary education, high school education, collegiate text competence, and linguistic expertise.
The preprocessing phase involved several steps to purify the dataset and improve classifier performance:
- Tokenizing: Splitting text into word tokens for processing.
- Filtering: Eliminating punctuation, non-Arabic characters, and other irrelevant data.
- Normalizing: Standardizing certain Arabic letters.
- Stop words Removal: Excluding common function words without significant classification utility.
- Stemming: Applying light stemming to reduce words to base forms by removing affixes, without altering root meaning significantly.
Methodology
The authors employed a supervised classification framework, deploying various machine learning algorithms:
- Multinomial Naïve Bayes
- Bernoulli Naïve Bayes
- Logistic Regression
- Support Vector Machine (SVM)
- Random Forest
The model utilized statistical representations of text data, employing both Count and TF-IDF vectors, with unigrams and bigrams as features. The task was formulated as a multi-class classification problem, aiming to maximize the conditional probability of associating a document with its corresponding readability level.
Experimental Evaluation
The experiments were conducted using 80% of the data for training and the remaining 20% for testing. The key findings indicated that:
- Multinomial Naïve Bayes achieved the highest accuracy with Count Vector representations, peaking at 86.47%.
- Support Vector Machine excelled with TF-IDF Vectors, with a top accuracy of 87.14%.
- Using a combination of unigrams and bigrams consistently provided better classification accuracy across both vector types compared to unigrams or bigrams alone.
Discussion and Conclusions
The experimental results underscore the effectiveness of n-gram features in readability classification, which significantly enhances predictive performance. The paper reveals notable insights regarding data representation, as TF-IDF Vectors displayed superior performance over Count Vectors in certain conditions. The authors advocate for further improvements by expanding the corpus, incorporating deeper linguistic features, and refining granularity in difficulty levels.
This work contributes substantially to the relatively underexplored domain of Arabic text readability and proposes a machine learning-based framework that could be pivotal for educators and learners alike. The approach not only aids in selecting appropriate educational content but also holds potential benefits for understanding complexity in other text-related applications such as e-learning platforms and content generation.