Arabic Social Media Analysis and Translation
Bibliographic record
Abstract
Twitter, is considered as one of the famous social networking platform. It has become a very valuable information source for many Natural Language Processing (NLP) applications. Some strategies and linguistic pipelines were developed for analyzing English tweets but Arabic social media analysis is still an active research area. In this research paper, we focus on the task of pre-processing Arabic tweets, which can be regarded as a first step for any NLP application. We follow up with a statistical machine translation for Arabic tweets into English, where we explain the normalization process for both Arabic and English tweets. Moreover, to overcome the obstacle of unavailability of Arabic-English parallel corpora in the social media context, we used the UN corpus, a more general corpus in (Modern Standard Arabic and English). Then, we applied adapting strategies for the tweet’s contents like using an out-of-domain and/or in-domain language model. Our conducted experiments showed that applying a good lexical normalization on both languages and combining in-domain and out-of-domain data for the language model improves the Bleu score with 4pt., over the baseline.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".