Enhanced Arabic Information Retrieval by Using Arabic Slang Language
Bibliographic record
Abstract
Slang language has become the most used language in the most countries. It has almost become the first language in the social media, websites and daily conversations. Moreover, it has become used in many conferences to clarify information and to deliver the required purpose of them. Therefore, this great spread of slang language over the world. In Jordan indicates that it is important to know meanings of Jordanian slang vocabularies. Mainly, In research system, we created a system framework allows users to restore Arabic information depending on queries that are written in slang language and this framework was made basically by context-free grammar to convert from slang to classical and vice versa. In addition, to conclude with, we will apply it on the colloquial slang in North of Jordan specifically; Irbid, Ajloun, Jerash, Mafraq and AlRamtha city. As well as, we will make a special file for Non_Arabic words and the stop words too. After we made an evaluation for the system relying on the results of recall, precision and F-measure where the results of precision about 0.63 for both researches slang and classical query, and this indicates that the system supports searching in Jordanian slang language. The purpose of this research is to enhance Arabic information retrieval, and it will be a significant resource for researchers who are interested in slang languages. As well as, it helps tie communities together.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".