KEYWORD EXTRACTION STRATEGY FOR ITEM BANKS TEXT CATEGORIZATION
Why this work is in the frame
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Bibliographic record
Abstract
We proposed a feature selection approach, Patterned Keyword in Phrase ( PKIP ), to text categorization for item banks. The item bank is a collection of textual question items that are short sentences. Each sentence does not contain enough relevant words for directly categorizing by the traditional approaches such as “bag‐of‐words.” Therefore, PKIP was designed to categorize such question item using only available keywords and their patterns. PKIP identifies the appropriate keywords by computing the weight of all words. In this paper, two keyword selection strategies are suggested to ensure the categorization accuracy of PKIP. PKIP was implemented and tested with the item bank of Thai high primary mathematics questions. The test results have proved that PKIP is able to categorize the question items correctly and the two keyword selection strategies can extract the very informative keywords.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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