Arabic Social Media Analysis and Translation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it