A Semi-Supervised Training Method for Semantic Search of Legal Facts in Canadian Immigration Cases
Why this work is in the frame
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Bibliographic record
Abstract
A semi-supervised approach was introduced to develop a semantic search system, capable of finding legal cases whose fact-asserting sentences are similar to a given query, in a large legal corpus. First, an unsupervised word embedding model learns the meaning of legal words from a large immigration law corpus. Then this knowledge is used to initiate the training of a fact detecting classifier with a small set of annotated legal cases. We achieved 90% accuracy in detecting fact sentences, where only 150 annotated documents were available. The hidden layer of the trained classifier is used to vectorize sentences and calculate cosine similarity between fact-asserting sentences and the given queries. We reached 78% mean average precision score in searching semantically similar sentences.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it