Ending-based Strategies for Part-of-speech Tagging
Bibliographic record
Abstract
Probabilistic approaches to part-of-speech tagging rely primarily on whole-word statistics about word/tag combinations as well as contextual information. But experience shows about 4 per cent of tokens encountered in test sets are unknown even when the training set is as large as a million words. Unseen words are tagged using secondary strategies that exploit word features such as endings, capitalizations and punctuation marks. In this work, word-ending statistics are primary and whole-word statistics are secondary. First, a tagger was trained and tested on word endings only. Subsequent experiments added back whole-word statistics for the words occurring most frequently in the training set. As grew larger, performance was expected to improve, in the limit performing the same as word-based taggers. Surprisingly, the ending-based tagger initially performed nearly as well as the word-based tagger; in the best case, its performance significantly exceeded that of the word-based tagger. Lastly, and unexpectedly, an effect of negative returns was observed - as grew larger, performance generally improved and then declined. By varying factors such as ending length and tag-list strategy, we achieved a success rate of 97.5 percent.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.008 |
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".