An ensemble approach for text document clustering using Wikipedia concepts
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
Most text clustering algorithms represent a corpus as a document-term matrix in the bag of words model. The feature values are computed based on term frequencies in documents and no semantic relatedness between terms is considered. Therefore, two semantically similar documents may sit in different clusters if they do not share any terms. One solution to this problem is to enrich the document representation using an external resource like Wikipedia. We propose a new way to integrate Wikipedia concepts in partitional text document clustering in this work. A text corpus is first represented as a document-term matrix and a document-concept matrix. Terms that exist in the corpus are then clustered based on the document-term representation. Given the term clusters, we propose two methods, one based on the document-term representation and the other one based on the document-concept representation, to find two sets of seed documents. The two sets are then used in our text clustering algorithm in an ensemble approach to cluster documents. The experimental results show that even though the document-concept representations do not result in good document clusters per se, integrating them in our ensemble approach improves the quality of document clusters significantly.
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 | 0.001 |
| 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