RELATION EXTRACTION METHOD OF CHINESE MEDICAL TEXT BASED ON RSIG-LSTM, 1-12.
Bibliographic record
Abstract
Purpose-long-short term memory (LSTM) is widely used in relation extraction.Tanh activation function in LSTM faces a vanishing gradient problem, which can hinder the transmission of knowledge and cause errors in the experimental results.Design/methodology/approach-in this paper, we propose a relation extraction method based on RSigELUS-LSTM (RSig-LSTM).Firstly, we use the bidirectional encoder representation from transformers (BERT) network model to embed word information.Secondly, we combine bidirectional RSig-LSTM with an attention mechanism to process features.We use the Softmax classifier to determine the relation type between entities in the Chinese medical text.Findings-Compared with LSTM and other improved LSTM, the precision of RSig-LSTM rose by 0.96%-5.25%,recall of RSig-LSTM rose by 0.25%-5.25%,F1-Score of RSig-LSTM rose by 0.66%-5.29%and time cost of RSig-LSTM has reduced by 6.97%-33.31%.Originality/value-A local dataset is used to reflect people's physical condition and enhance the practicability of our research.Considering the importance of medical-related entities in medical text research, we use a new formula to calculate the weight of the word in Chinese medical text.
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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 | 0.000 | 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".