Model Based Document Classification and Clustering
Bibliographic record
Abstract
In this paper we develop a complete methodology for document classification and clustering. We start by investigating how the choice of document features influences the performance of a document classifier and then use our findings to develop a clustering method suitable for document collections. From our study of the effect of frequency transformation, term weighting and dimensionality reduction through principal components analysis on the performance of a simple K-nearest-neighbors classifier, we conclude that: (a) applying a logarithm or square-root transformation to the term frequencies reduces error rates; (b) term weighting of the transformed frequencies does not appear to help much; and (c) increasing the feature space dimension beyond 50 does not improve performance. We use these findings in the construction of a Gaussian Mixture Document Clustering (GMDC) algorithm. This algorithm models the data as a sample from a Gaussian mixture. The model is used to build clusters based on the likelihood of the data, and to classify documents according to Bayes rule. One main advantage of our approach is the ability to automatically select the number of clusters present in the document collection. Our experiments with the Topic Detection and Tracking Corpus demonstrates the ability of GMDC to choose a sensible number of clusters and to generate meaningful partitions of the data.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".