qhjqhj00 legal-ner-eval
SemEval 2023 Task 6: LegalEval - Understanding Legal Texts — Modi et al. (2023) (arXiv:2304.09548, 2023)
What this evaluates
Evaluates a model's capacity to identify and classify 14 domain-specific legal entities (e.g., Court, Statute, Precedent, Petitioner Name) within unstructured legal documents. This probes fine-grained information extraction capabilities tailored to legal terminology and structure.
Datasets
- LegalEval L-NER Dataset — total ?; splits: test (-1)
Metrics
standard F1 score(primary) — range: [0, 1]- Standard token-level or span-level F1 score for named entity recognition, balancing precision and recall across all entity types.
Input / output format
Input: A legal judgment document containing text with potential legal entities.
Output: A sequence of named entity tags (e.g., BIO format) or extracted spans with corresponding entity types.
Scoring recipe
def compute_ner_f1(predictions, gold):
pred_spans = extract_spans(predictions)
gold_spans = extract_spans(gold)
tp = len(set(pred_spans) & set(gold_spans))
fp = len(set(pred_spans) - set(gold_spans))
fn = len(set(gold_spans) - set(pred_spans))
prec = tp / (tp + fp) if (tp + fp) > 0 else 0
rec = tp / (tp + fn) if (tp + fn) > 0 else 0
return 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
Common pitfalls
- Legal entities often overlap or have nested structures, requiring careful span alignment.
- Standard NER models fail on domain-specific types like 'Statute' or 'Precedent' without legal fine-tuning.
Evidence (verbatim from paper)
For L-NER, we use standard F1 score metrics
Citation
@misc{modi2023legaleval,
title={SemEval 2023 Task 6: LegalEval - Understanding Legal Texts},
author={Modi et al. (2023)},
year={2023},
note={arXiv:2304.09548}
}
- arXiv: 2304.09548