An Approach for Text Categorization in Digital Library
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Text categorization is a very effective way to organize enormous number of documents in Digital Libraries. Accurate classification of documents is able to not only enhance document search precision, but also facilitate browsing-by- topic functionality. It is, nonetheless, difficult to obtain a satisfactory categorization accuracy compared to the corresponding results given by professional catalogers. This is due largely to the complexity of the pre-defined large-scaled category hierarchies that makes it difficult for learning algorithms to distinguish among categories. This paper describes a top-down document classification approach which takes advantage of the hierarchical structure, more specifically, in two ways: identifying the number of independent local classifiers and guiding top-down classification procedure. We finally evaluate it within the CINDI Digital Library applying ACM Classification System as targeted hierarchy. Experimental results show the promise of this approach.
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 | 0.000 | 0.000 |
| 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 | 0.002 |
| 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