Statute Law Information Retrieval and Entailment

By Mi-Young Kim, Juliano Rabelo, Randy Goebel. 2019

34 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Political Science and International Relations, Natural Language Processing

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Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “et al., 2019a]. However, more explorations are clearly needed: for task 3, of the seven participating teams, none used BERT-based models and the best submissions were based on exact match techniques [Kim et al., 2019]. In task 4, there were also seven participating teams, and the best submission was”
A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering (2020, unknown): “The intersection between NLP and the legal domain is a growing area of research [3, 10, 32, 34, 47], but with few large-scale systematic resources.”
A Summary of the COLIEE 2019 Competition (2019, methodology): “– UA [14] uses condition/conclusion/exception detection rules, and negation dictionaries created manually.”