Bibliographic record
Abstract
Law is one of the knowledge domains that are most reliant on textual material. Nowadays, however, it is very difficult and time-consuming for legal professionals to read, understand, and analyze all the available documents, due to the vast volume of case law that is published every day. In this age of legal big data, and with the increased availability of legal text online, many researchers have given more focus to the development of legal intelligent systems and applications. These intelligent systems can provide great services and solve many problems in legal domain. Over the last years, researchers have focused on predicting judicial case outcomes using Natural Language Processing (NLP) and Machine Learning (ML) methods over case documents. Thus, Legal Judgment Prediction (LJP) is the task of automatically predicting the outcome of a court case given only the text of the case. To the best of our knowledge, no prior research with this intention has been conducted in English for appeal courts in Canada, as of 2021. The NLP application to legal judgments, that our proposed methodology focuses on, is to predict the outcomes of cases by looking only at the description of cases written by the court. Because appeal court decisions are often binary, as in accept or reject, the task is defined as a binary classification problem between’ Allow’ and ‘Dismiss'. This is the general approach in the literature as well. We employ various classification methods including classical classifiers, Deep Learning (DL) models, and compare their performances. Our best results are obtained using DL models with accuracy values reaching 93.46% and F1-scores reaching 0.92, which are on par with the best results in the literature. Through this study, we hope to establish the basis for future research on the legal system of Canada and offer a baseline for future work.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".