A New Algorithm for Arabic Document Clustering Utilizing Maximal Wordsets
Bibliographic record
Abstract
Arabic document clustering (ADC) is a critical task in Arabic Natural Language Processing (ANLP), with applications in text mining, information retrieval, Arabic search engines, sentiment analysis, topic modeling, document summarization, and user review analysis.In spite of the critical needs of ADC, the available ADC algorithms achieved limited success based on the evaluation metrics used for clustering.This paper proposes a novel method for clustering Arabic documents.The method leverages Maximal Frequent Wordsets (MFWs).The MFWs are extracted using the FPMax algorithm, a data mining technique adept at identifying significant recurring word patterns within the documents.These MFWSs serve as features for a new clustering approach that groups documents based on content similarity.Each MFW serves as a data structure housing features, their respective strengths in clustering, and the corresponding documents, simplifying the clustering process to a mere measurement of similarity.The proposed approach offers various clustering results for varying numbers of clusters in one training session.The effectiveness of the proposed method is assessed using two well-known benchmark datasets (CNN and OSAC), achieving accuracy of 80% and 81% respectively.This approach offers a promising contribution to the field of ANLP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".