Retrieving Arabic Textual Documents Based on Queries Written in Bahraini Slang Language
Bibliographic record
Abstract
Nowadays, the most used language is the colloquial language not the classical language. It is widely used in many nations. The kingdom of Bahrain had the largest share in the spread of the colloquial language, which becomes the trader's language and the language of the social communication too. It became so popular that its usage starts dominating the daily conversations. In this research, we will create algorithm to enhance the process of information retrieval in Arabic slang language of the Gulf. In this algorithm, we put some special Bahraini rules to convert queries from Slang Bahraini to a classical language. In addition, we will apply this algorithm on the Bahraini colloquial language. After making an evaluation for the system relying on the results of three main aspects recall, precision, and F-measure, we noticed that the results of precision about 0.64 for both researches slang and classical, which gives a great indication that the system supports searching in Bahraini slang language. The purpose of this research is to improve the Information Retrieval system field. In addition, it will save the time and the effort of the researchers of the Bahraini colloquial language.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".