Grammatical and Semantic Disambiguation of Requirements at Elicitation and Representation Stages
Bibliographic record
Abstract
The final outcome of a design process depends a lot on the initial conditions of this process. The initial design conditions can be viewed as the initial definition and representation of the design problem in the form of requirement model. Describing the requirements involves considering their elicitation and its transformation in a form that can be further used by engineering designers. These two phases of requirements, elicitation and representation, involve by nature linguistic description. Users, stakeholders or designers express themselves through natural language. Semantics considerations involve understanding aspects that comes down to word selection or connotation but also interpretation aspects of written terms used by communities or persons within particular circumstances and contexts. The present research work is constructed around a central hypothesis: Final design outcomes are strongly dependent on the initial design conditions because of the recursive nature of the design activity. The present article claims that computer tools can support the disambiguation process associated with elicitation and representation. For this reason the authors have developed an experimental process aiming at reducing ambiguity of the parts of the initial conditions of the design process that are expressed in natural language. This disambiguation is considering several levels: the grammar, words selection and context description.
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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 | 0.028 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".