Bibliographic record
Abstract
Plains Cree (nêhiyawêwin) is a morphologically complex and predominantly prefixing language.The combinatory potential of inflectional and derivational/lexical prefixes and verb stems in Plains Cree makes it challenging for traditional auto-completion (or word suggestion) approaches to handle.The lack of a large corpus of Plains Cree also complicates the situation.This study attempts to investigate how well a BiLSTM model trained on a small Cree corpus can handle a word suggestion task.Moreover, this study evaluates whether the use of semantically and morphosyntactically refined Word2Vec embeddings can improve the overall accuracy and quality of BiLSTM suggestions.The results show that some of the models trained with the refined vectors provide semantically and morphosyntactically better suggestions.They are also more accurate in predictions of content words.The model trained with the non-refined vectors, in contrast, was better at predicting conjunctions, particles, and other non-inflecting words.The models trained with different refined vector combinations provide the expected next word among top-10 predictions in 36.32 to 37.34% of cases (depending on the model).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".