The Constrained Interaction Network for Aspect-level Sentiment Classification Task
Bibliographic record
Abstract
The purpose of aspect-level sentiment classification is to predict the sentiment polarity of specific aspect words in a sentence. Recently many works exploit LSTM models based on the attention mechanism. However, the prior work only attends to using the aspect terms to capture the aspect-specific sentiment information in the text. It may cause the mismatch of sentiment when the aspect words are extracted incorrectly. To solve this problem, we propose a simple but effective framework called the Constrained Interaction Network(CIN), which consists of the context-aspect level interaction layer(CAI-Layer), the long and short-term memory network layer(LSTM-Layer), and Constraint Attention layer(CA-Layer). CIN can extract the sentiment features of specific aspects with the assistance of LSTM-Layer and CAI-Layer, which greatly share the attention layer. The experiment conducted on three widely used data sets in SemEval 2014 and Twitter shows that the constrained attention mechanism is always better than other existing attention mechanisms, which also confirms that the CA- Layer can indeed help LSTM to extract the specified aspect-level sentiment characteristics.
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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 | 0.001 | 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 | 0.001 | 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".