A Study on Modeling and Computing of Lexical Semantic Relationships in Japanese Language Based on Multidimensional Vector Space
Bibliographic record
Abstract
Multidimensional vector space is the basis of lexical semantic correlation computation, which is able to assess the similarity between lexical semantics. In this paper, we implement a Japanese lexical named entity recognition and semantic relation calculation method based on this method. Dependency relations are fitted using N-Gram and knowledge expansion, contextual relations are corrected using collocation frequency, and semantic interactions are determined by semantic linking methods. The accuracy and recall of the identification of this method are higher than that of the spatial semantic role method by 0.78% and 4.93%, respectively, and the quantized values of the calculated correlations accurately reflect the strong and weak lexical semantic relationships. The results of the disambiguation experiments show that the maximum correlations computed using the method of this paper are consistent with the corresponding semantic items. Therefore, the method designed in this paper for recognizing named entities and calculating semantic relations of Japanese words has a relatively accurate recognition rate of semantic relations and has the ability of disambiguation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".