On Semantic and Syntactical Selective Constraints among Multiple Words in Context
Bibliographic record
Abstract
The semantic and syntactical selective constraints among multiple words in context are surveyed. Main types of selective constraints in context are: selective constraint between immediate constitutes, the one among multiple words in the same sentence, the one among multiple words in different sentences and that between words and situational factors. And they are further grouped into different categories. The knowledge of these selective constraints must be used in syntactical analysis of natural language processing for disambiguation. Keywords: context, multiple words, selective constraints, disambiguation Resume Cet essai travaille sur les relations selectives et contraignantes des expressions dans le texte sous l’angle semantique et syntaxique. Les principales selections et contraintes des expressions viennent des mots voisins, du troisieme mot de la meme phrase, du troisieme mot d’une autre phrase, du contexte, etc. L’auteur fait une classification de ces selections et contraintes. On doit recourir aux selections et contraintes de differents niveux pour eliminer les interpretations differentes dans l’analyse syntaxique. Mots-cles: texte, expressions, selections et contraintes, elimination des interpretaions differentes 摘要 本文在語篇中考察多詞項間的語義句法選擇限制關係。語篇中的詞項所受到的選擇限制主要有：句 內直接成分間的選擇限制、句內第三個詞項的選擇限制、跨句中第三個詞項的選擇限制、以及語境因素的選 擇限制等，並對這些選擇限制作了歸類。句法分析中必須利用各種層面的選擇限制信息來進行歧義消解。 關鍵詞：中文摘要語篇；多詞項；選擇限制；歧義消除
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".