Issue Area Discovery from Legal Opinion Summaries using Neural Text Processing

Avi Bleiweiss

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Paper

Issue Area Discovery from Legal Opinion Summaries using Neural Text Processing

Topics: Deep Learning; Legal Technologies; Natural Language Processing; Neural Networks

In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 938-944, 2022

Author: Avi Bleiweiss

Affiliation: BShalem Research, Sunnyvale, U.S.A.

Keyword(s): Legal Domain, Issue Area Prediction, Transformers, Language Model, Deep Learning.

Abstract: Applying existed methods of language technology for classifying judicial opinions into their respective issue areas, often requires annotation voting made by human experts. A tedious task nonetheless, further exacerbated by legal descriptions consisting of long text sequences that not necessarily conform to plain English linguistics or grammar patterns. In this paper, we propose instead a succinct representation of an opinion summary joined by case-centered meta-data to form a docket entry. We assembled over a thousand entries from court cases to render our low-resourced target legal domain, and avoided optimistic performance estimates by applying adversarial data split that ensures the most dissimilar train and test sets. Surprisingly, our experimental results show that fine-tuning a pretrained model on standard English recovers issue area prediction by 9 and 8 F1 percentage points over a pretrained model on the legal domain, for macro and weighted average scores, respectively.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Bleiweiss, A. (2022). Issue Area Discovery from Legal Opinion Summaries using Neural Text Processing. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 938-944. DOI: 10.5220/0010974300003116

@conference{icaart22,
author={Avi Bleiweiss.},
title={Issue Area Discovery from Legal Opinion Summaries using Neural Text Processing},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={938-944},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010974300003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Issue Area Discovery from Legal Opinion Summaries using Neural Text Processing
SN - 978-989-758-547-0
IS - 2184-433X
AU - Bleiweiss, A.
PY - 2022
SP - 938
EP - 944
DO - 10.5220/0010974300003116
PB - SciTePress