Bibliographic record
Abstract
In this paper, we present a novel approach to text mining that helps to build intelligent user interfaces for recommender and information retrieval systems. The main problem for the user in information retrieval is that he must have almost perfect knowledge of the domain and the domain terminology. Our approach eases this burden by showing a way how to encode domain knowledge so that an information retrieval system can transform the user's way to talk about the domain in the expert's way to do that. After that transformation the system can search its data bases for appropriate information. We demonstrate the practicability of our approach in a case study on a TV recommender system. RESUME. Le present article introduit une nouvelle approche dans le domaine de la fouille tex- tuelle, dans le but de faciliter le developpement d'intelligentes interfaces aux utilisateurs pour systemes de recommandation ainsi que de recherche documentaire. En recherche documentaire, le principal probleme pour les utilisateurs consiste a devoir disposer de connaissances quasi- ment parfaites du domaine d'application et de sa terminologie. Notre approche vient attenuer cette requisition en montrant une facon d'encoder les connaissances de domaines d'application de maniere a ce que les systemes de recherche documentaire puissent transformer la terminolo- gie (relative aux domaines) des utilisateurs en celle des experts des domaines respectifs. Cette transformation effectuee, les systemes peuvent consulter leurs bases de donnees pour trouver les informations recherchees. La faisabilite de notre approche est demontree par l'etude de cas d'un systeme de recommandation d'emissions de television.
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.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".