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
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it