The Design and the Construction of the Traditional Arabic Lexicons Corpus (The TAL-Corpus)
Why this work is in the frame
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Bibliographic record
Abstract
Arabic lexicography is a well-established and deep-rooted art of Arabic literature. Computational lexicography, invests computational and storage powers of modern computers, to accelerate long-term efforts in lexicographic projects. A collection of 23 machine-readable dictionaries, which are freely available on the web, were used to build the Corpus of Traditional Arabic lexicons (the TAL-Corpus). The purpose for constructing the TAL-Corpus is to collect and organize well-established and long traditions of traditional Arabic lexicons which can also be used to create new corpus-based Arabic dictionaries. The compilation of the TAL-Corpus followed standard design and development criteria that informed major decisions for corpus creation. The corpus building process involved extracting information from disparate formats and merging traditional Arabic lexicons. As a result, the TAL-Corpus contains more than 14 million words and over 2 million word types (different words).  The TAL-Copus was applied to create useful morphological database. This database was automatically constructed using a new algorithm which is informed by Arabic linguistics theory. The newly developed algorithm processed the text of the TAL-Corpus and as result it extracted 2 781 796 entries. These entries were stored in the morphological database where each represents a word-root pair (i.e. an Arabic word and its root). A comparative evaluation of the TAL-Corpus and other three Arabic corpora showed that the lexical diversity of its vocabulary scored higher. Moreover, its coverage was computed by comparing words and lemmas against their equivalents of other corpora where it scored about 67% when comparing words and 82% when comparing lemmas.
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.
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 | 0.003 | 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 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| 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