Discovering significant topics from legal decisions with selective inference

Discovering significant topics from legal decisions with selective inference

We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes,...

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Bibliographic Details
Main Author: SOH, Jerrold Tsin Howe
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
domain name disputes
European Court of Human Rights
legal language processing
text-as-data
topic models
Courts
Legal Studies
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sol_research/4433
https://ink.library.smu.edu.sg/context/sol_research/article/6391/viewcontent/Discovering_significant_topics_from_legal_decisions_with_selective_inference_pvoa_cc_by.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sol_research/4433
https://ink.library.smu.edu.sg/context/sol_research/article/6391/viewcontent/Discovering_significant_topics_from_legal_decisions_with_selective_inference_pvoa_cc_by.pdf

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