Use of Keyphrase Extraction Software for Creation of an AEC/FM Thesaurus
Bibliographic record
Abstract
The paper describes a method used to collect terms needed for the development of a thesaurus in the roofing domain. This work is part of a larger effort to investigate the potential of thesauri as an aid in product modeling and as a tool for information management in model-based systems. Extractor, a software module that extracts keyphrases from documents, was used for collecting candidate thesaurus terms from Internet sources. The principal advantage of the Internet as a source of candidate terms is that it reflects the language that is actually used in communications concerning buildings and that it covers the widest range of different views on the domain. The advantage of using Extractor or similar software is that it allows processing huge text corpora available on the Internet while eliminating irrelevant terms. The methodology used was found to be highly useful, although it was not sufficient by itself for constructing a thesaurus for the architecture, engineering, construction and facilities management industries, as considerable human intervention was required. Some possibilities for customizing the software and for partially automating a thesaurus construction process are suggested.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".