Cloud computing architecture for Tagging Arabic Text Using Hybrid Model
Bibliographic record
Abstract
With the increasing role of technology in transferring information in our daily lives, the Arabic language has become the fourth language used on the Internet. Therefore, to develop different information systems in the Arabic language, we should determine the syntax and semantics of creating a text efficiently and accurately. Part of speech (POS) is one of the primary methods employed to develop any language corpus. Each language consists of several tags applied in different applications, such as natural language processing (NLP), speech synthesis, and information extraction. One of the main benefits of adopting cloud computing services is the offer a low cost and time to store your company data compared to traditional methods. This paper presents and deploys a cloud computing architecture for Tagging Arabic text using a hybrid model, which will help reduce the efforts and cost. The results show an excellent accuracy rate in tagging an Arabic text and quickly respond. Previous studies are compared based on relevant rating factors, which achieved high accuracy, procession, and recall rate of more than 95%. The cloud computing tagger attained an accuracy of 99.2%.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".