Semantic Annotation of Legal Contracts with ContrattoA
Why this work is in the frame
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Bibliographic record
Abstract
The aim of the research is to semi-automate the process of generating formal specifications from legal contracts in natural language text form. Towards this end, the paper presents a tool, named ContrattoA, that semi-automatically conducts semantic annotation of legal contract text using an ontology for legal contracts. ContrattoA was developed through two iterations where lexical patterns were defined for legal concepts and their effectiveness was evaluated with experiments. The first iteration was based on a handful of sample contracts and resulted in defining lexical patterns for recognizing concepts in the ontology; these were evaluated with an empirical study where one group of subjects was asked to annotate legal text manually, while a second group edited the annotations generated by ContrattoA. The second iteration focused on the lexical patterns for the core contract concepts of obligation and power where results of the first iteration were mixed. On the basis of an extended set of sample contracts, new lexical patterns were derived and those were shown to substantially improve the performance of ContrattoA, nearing in quality the performance of experts. The experiments suggest that good quality annotations can be generated for a broad range of contracts with minor refinements to the lexical patterns.
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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 | 0.001 | 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 it