Arabic Text Dataset

Project Overview:

Objective

Compile an expansive dataset of Arabic text tailored for a spectrum of linguistic and machine learning applications, emphasizing accuracy, diversity, and cultural nuances.

Scope

Collection of Arabic text from multiple genres, with intricate annotations to ensure precision and richness.

Sources

Literary genres encompass poetry, fiction, and non-fiction are collected.
Effectively collected news articles and broadcasts disseminating information.
Scholarly research papers produced within academia.
Discussions were collected on online forums and posts on social media.
Texts with historical and religious significance are curated effectively.

Data Collection Metrics

Total Text Entries: 300,000
Literary Works: 80,000
News Articles: 70,000
Academic Papers: 50,000
Online Conversations: 60,000
Historical & Religious Texts: 40,000

Annotation Process

Stages

POS Tagging: Labeling parts of speech for each word.
Named Entity Recognition: Identifying and classifying names, places, dates, etc.
Sentiment Analysis: Tagging sentences or passages with their emotional tone.
Dialect Identification: Labeling based on regional Arabic dialects (e.g., Egyptian, Gulf, Levantine).
Morphological Analysis: Breaking down words into their root, pattern, and affixes.

Annotation Metrics

Total Annotations: 1,500,000
POS Tags: 500,000
Sentiments: 200,000
Dialect Tags: 250,000
Morphological Tags: 250,000

Quality Assurance

Linguist Review: Engaging native Arabic linguists to validate annotations.

Consistency Audits:Automated tools to ascertain uniformity across annotations.

Inter-annotator Agreement: Assigning overlapping sections to multiple annotators to ensure consistent tagging.

QA Metrics:

Annotations Reviewed by Linguists: 150,000 (10% of total annotations)
Inconsistencies Detected and Rectified: 30,000 (2% of total annotations)

Conclusion

The Arabic Text Dataset initiative has led to the creation of a resource rich in cultural, academic, and linguistic diversity. Through meticulous collection and annotation, this dataset stands out as a beacon for Arabic language studies, AI training, and linguistic research. Its vastness and depth are sure to contribute significantly to advancing Arabic natural language processing and understanding.