User-relevant access to textual information through flexible identification of terms: a semi-automatic method and software based on a combination of n-grams and surface linguistic filters
Bibliographic record
Abstract
We present a semi-automatic method and software tool for multi-word term identification. Our approach is hybrid in that it combines numeric computations (N-grams) to linguistic filters. The software tool is different from most other term identification tools in that is it by design semi-automatic: i.e. it is interactive and constantly under the user's control. The software supports the knowledge engineer's work, the (corpus) domain's expert, or the linguist, by helping them do their job more efficiently. We justify this semi-automatic approach by the need to have a more flexible and customisable tool to perform certain term identification tasks. More specifically, in some applications we want to allow the user's perspective, knowledge and subjectivity, influence the results: all this within certain limits, of course. An example of such an application on which we are currently working is that of Web personalisation: to allow individuals to develop their own vision of information univer...
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".