Graphical Learning Algorithm Based Analysis of Entity Relationships in Legal Texts
Bibliographic record
Abstract
The continuous improvement of judicial construction has led to the emergence of a large amount of judicial data on the Internet, and how to make full use of judicial data to promote judicial openness, fairness and efficiency has become an important issue in the construction of judicial informatization.In the article, the word vector generation technique is used to obtain the annotation sequence of legal text, and then the BiLSTM model is combined with the CRF model to realize the recognition of legal text entities, and the Adam algorithm is used to optimize the training of the model, so as to improve the recognition effect of the model on legal text entities.The GCN model in the graph representation learning algorithm is introduced, and the legal text entity recognition results are used as inputs for the construction of sequential and semantic relationships, and the GCN-BiLSTM model for legal text entity relationship extraction is constructed by combining the graph representation attention network and the BiLSTM model.Based on the self-constructed legal text dataset, the validation analysis of the above model is carried out through simulation experiments.The accuracy of the BILSTM-CRF model in legal text entity recognition is 85.67%, which is 7.35% higher than that of the single LSTM-CRF model.The GCN-BiLSTM model improves its accuracy by 2.14 percentage points compared with the CasRel model in extracting the entity relationships of legal texts with multi-entity overlapping.Combined with the legal text entity relationship extraction results, the knowledge map of legal cases can be constructed to provide accurate knowledge relationship support for sorting out the veins of legal cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".