Improving Deep Belief Networks via Delta Rule for Sentiment Classification
Bibliographic record
Abstract
Sentiment classification has received much attention in both engineering and academic fields. Deep belief networks (DBN) has proved powerful in many domains including natural language processing. In this paper, DBN is applied in sentiment classification, while we propose a new way to improve the DBN based on the unsupervised training phase of restricted Boltzmann machines (RBM). That is, the RBM generates the hidden layer in an unsupervised fashion, and then we use this hidden layer as the output of a single-layer neural network, which is trained using the delta rule. The new weights trained from delta rule are then transmitted into the whole back propagation. This way keeps much more correction signal information for each layer in back propagation compared to that in the same network structure. Consequently, our experimental results demonstrate that the new learning method performs relatively better on ten sentiment datasets, which further proves the delta rule improves DBN performance for natural language processing tasks.
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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 | 0.000 |
| 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".