Document classification using convolutional neural networks with small window sizes and latent semantic analysis
Bibliographic record
Abstract
A parsimonious convolutional neural network (CNN) for text document classification that replicates the ease of use and high classification performance of linear methods is presented. This new CNN architecture can leverage locally trained latent semantic analysis (LSA) word vectors. The architecture is based on parallel 1D convolutional layers with small window sizes, ranging from 1 to 5 words. To test the efficacy of the new CNN architecture, three balanced text datasets that are known to perform exceedingly well with linear classifiers were evaluated. Also, three additional imbalanced datasets were evaluated to gauge the robustness of the LSA vectors and small window sizes. The new CNN architecture consisting of 1 to 4-grams, coupled with LSA word vectors, exceeded the accuracy of all linear classifiers on balanced datasets with an average improvement of 0.73%. In four out of the total six datasets, the LSA word vectors provided a maximum classification performance on par with or better than word2vec vectors in CNNs. Furthermore, in four out of the six datasets, the new CNN architecture provided the highest classification performance. Thus, the new CNN architecture and LSA word vectors could be used as a baseline method for text classification tasks.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".