Bibliographic record
Abstract
Life service information plays an important role in people’s life, such as weather conditions, so the study of how to get lifeservice information has important significance. This paper put forward a question processing method called “integrated semanticalgorithm” in Q&A System of life service information. The new algorithm was based on the semantic web, word order similarityalgorithm and the syntactic similarity algorithm. When matching the question templates, especially for some question templateswhich are characteristic of certain fields, the new algorithm can identify the type of questions, narrow the matching range of thequestion templates, and improve the matching accuracy. In the experiment, we chose “weather field” as the experimental subject.In the first experiment, we built the question syntactic templates and semantic web of weather, and collected 55 questions ofweather title as test set. Then we used the word similarity algorithm, the syntactic similarity algorithm and integrated semanticsimilarity algorithm to match question templates with the test question set. The experimental results show that the integratedsemantic algorithm is better than the other two algorithms in matching accuracy. In the second experiment, we randomlyselected some questions from different fields, then we used the three similarity algorithms in the first experiment to do the fielddistinguishing experiment. The experiment shows that only the integrated semantic algorithm can recognize questions of differentfields.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".