Statute Law Information Retrieval and Entailment
Bibliographic record
Abstract
Our Yes/No statute law question answering system combines components for both statute law information retrieval and confirmation of textual entailment between statues and legal questions. We describe a statute law question answering system that exploits TF-IDF and a language model for information retrieval, and inter-paragraph entailment. We have evaluated our system using the data from the competition on legal information extraction/entailment (COLIEE-2019). The competition consists of four tasks: Tasks 1 and 2 are for the case law information extraction/entailment, and Tasks 3 and 4 are for the statute law information extraction/entailment. Here we explain our methods and evaluation results for Tasks 3 and 4. Task 3 requires the identification of civil law articles relevant to Japan legal bar exam query. For this task, we used TF-IDF and language model-based information retrieval approaches. Task 4 requires a decision on yes/no answer for previously unseen queries given relevant civil law articles. Our approach compares the approximate meanings of queries with relevant articles. Because many statute law and queries consist of more than one paragraph, we need an inter-paragraph entailment method. Our inter-paragraph entailment process exploits an analysis of statute law structure, and negation patterns to predict entailments. Using our heuristic selection of attributes, we perform two experiments which provide the basis for making a decision on the yes/no questions. One experiment uses an SVM model, and the other uses a general heuristic rule. Our experimental evaluation demonstrates the value of our method, and the results show that our method was ranked No. 1 in both of the Tasks 3 and 4 in COLIEE 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".