COMPUTATIONAL REPRESENTATION OF LINGUISTIC SEMANTICS FOR REQUIREMENT ANALYSIS IN ENGINEERING DESIGN
Bibliographic record
Abstract
The objective of this research is to use computational linguistics to identify semantic implicit relationships between text-based relationships. Specifically, natural language processing is used to implement linguistic semantics in requirement analyzers. Linguistic semantics is defined as the meaning of words beyond their string form, part of speech, and syntactic function. Many existing design tools use part of speech tagging and sentence parsing as the foundation of their requirement analysis but ultimately use string algorithms to evaluate requirements. These string algorithms cannot capture the implicit knowledge in requirements. This research compares five methods of requirement analysis. A manual analysis provides the benchmark against which the subsequent analyzers are judged. A syntactic analysis is implemented and compared to the manual method to gain insight into the capabilities of current methods. The other three analyzers implement semantic tools for requirement analysis through semantic ontologies and latent semantic analyses. The results from the semantic analyzers are compared to the results of the other two analyzers to judge the capabilities of semantics in requirement analysis. The findings show that semantics can be identified with at least 74% accuracy. Further, the agreement between the semantic results and the manual results are more related than the syntax results and the manual results. While the implementation of semantics into requirement analysis does not completely agree with manual findings, the semantic analyses improve upon syntactic and string matching analyses used in current research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".