Arabic Text Dataset

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Project Overview:

Objective

To compile an expansive dataset of Arabic text tailored for a spectrum of linguistic and machine learning applications, it is essential to emphasize accuracy, diversity, and cultural nuances. Consequently, the process requires a comprehensive approach that incorporates various sources and text types.

Scope

The collection of Arabic text spans multiple genres, encompassing a wide array of literary forms and thematic expressions. To ensure precision and richness, intricate annotations have been meticulously added. Additionally, these annotations provide comprehensive insights, enhancing the reader’s understanding of the nuances within the text. Moreover, the diverse genres represented include poetry, prose, historical documents, and religious texts.

Sources

Literary genres encompass poetry, fiction, and non-fiction are collected. Additionally, within these broad categories, we find various subgenres that further enrich the literary landscape.
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: involves labeling parts of speech for each word. Additionally, Named Entity Recognition focuses on identifying and classifying names, places, dates, etc.
Named Entity Recognition: Furthermore, Sentiment Analysis entails tagging sentences or passages with their emotional tone.
Sentiment Analysis: Tagging sentences or passages with their emotional tone.
Moreover, Dialect Identification: Labeling based on regional Arabic dialects (e.g., Egyptian, Gulf, Levantine).
Lastly, 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

Stages

Linguist Review: Engaging native Arabic linguists to validate annotations.
Additionally, Consistency Audits: Automated tools to ascertain uniformity across annotations.
Furthermore, 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. Consequently, through meticulous collection and annotation, this dataset stands out as a beacon for Arabic language studies, AI training, and linguistic research. Furthermore, its vastness and depth are sure to contribute significantly to advancing Arabic natural language processing and understanding.