A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment
This paper introduces the Balanced Arabic Readability Evaluation Corpus BAREC, a large-scale, fine-grained dataset for Arabic readability assessment. BAREC consists of 68,182 sentences spanning 1+ million words, carefully curated to cover 19 readability levels, from kindergarten to postgraduate comprehension. The corpus balances genre diversity, topical coverage, and target audiences, offering a comprehensive resource for evaluating Arabic text complexity. The corpus was fully manually annotated by a large team of annotators. The average pairwise inter-annotator agreement, measured by Quadratic Weighted Kappa, is 81.3%, reflecting a high level of substantial agreement. Beyond presenting the corpus, we benchmark automatic readability assessment across different granularity levels, comparing a range of techniques. Our results highlight the challenges and opportunities in Arabic readability modeling, demonstrating competitive performance across various methods. To support research and education, we will make BAREC openly available, along with detailed annotation guidelines and benchmark results.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversitySimilar Papers 제목 키워드 기반
Guidelines for Fine-grained Sentence-level Arabic Readability Annotation
This paper presents the foundational framework and initial findings of the Balanced Arabic Readability Evaluation Corpus (BAREC) project, designed to address the need for comprehensive Arabic language resources aligned w…
BenchmarkingSentenceA Fine-Grained Annotated Multi-Dialectal Arabic Corpus
We present ARAP-Tweet 2.0, a corpus of 5 million dialectal Arabic tweets and 50 million words of about 3000 Twitter users from 17 Arab countries. Compared to the first version, the new corpus has significant improvements…
ARCADE: A City-Scale Corpus for Fine-Grained Arabic Dialect Tagging
The Arabic language is characterized by a rich tapestry of regional dialects that differ substantially in phonetics and lexicon, reflecting the geographic and cultural diversity of its speakers. Despite the availability …
Multi-Task LearningAutomatically Developing a Fine-grained Arabic Named Entity Corpus and Gazetteer by utilizing Wikipedia
Arabic Fine-Grained Entity Recognition
Traditional NER systems are typically trained to recognize coarse-grained entities, and less attention is given to classifying entities into a hierarchy of fine-grained lower-level subtypes. This article aims to advance …
NER