A word‐level language identification strategy for resource‐scarce languages
Bibliographic record
Abstract
ABSTRACT This study is based on the premise that it is possible to train computers to predict the language of a word (textual or audio) by learning from its character n‐gram pattern, without recourse to the language's dictionary. With the growth of multilingual collections and a need for automatic means of cleaning textual datasets, this paper presents a strategy for language identification of individual words in a body of texts. This strategy is suitable for resource‐scarce languages that do not have large electronic datasets that are required for machine learning and natural language processing studies and whose dictionaries may not be available. In this study, we focused on three African languages, namely Hausa, Igbo, and Yoruba. A training corpus in each of these languages was used to obtain the probabilities of character trigrams in the language. Given that English is a common language that is often mixed with these resource‐scarce languages in texts, we also obtained the probabilities of trigrams in an English training corpus. These probabilities were then used in identifying the language of each word in test corpora containing bilingual texts. Our strategy achieved average precision, recall and F1 values of about 97%, 91% and 94% respectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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 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".