Applying Word Embeddings for Lithuanian Morphology: The Case of Adjectival Participles
Bibliographic record
Abstract
This paper presents how word embeddings were used to identify adjectival Lithuanian participles.Although traditionally considered to be a form of a verb, participles in the Lithuanian language also have the characteristics of adjectives.The paper describes a study on how one of the criteria for the identification of adjectival participles was applied using the fastText word embedding model.This criterion involves the recognition of adjectives and pronouns that are semantically similar to participles (e.g., these adjectives and pronouns can be synonyms or antonyms of participles).The paper assesses the extent to which word embeddings can help to identify adjectival Lithuanian participles and summarises the advantages and disadvantages of this method.Out of 289 analysed participles, 48 participles (16.61%) that are semantically similar to adjectives and pronouns were identified using word embeddings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".