Analysis and extraction of useful information from legal judgments using
computational linguistics was one of the earliest problems posed in the domain
of information retrieval. Presently, several commercial vendors exist who
automate such tasks. However, a crucial bottleneck arises in the form of
exorbitant pricing and lack of resources available in analysis of judgements
mete out by Hong Kong's Legal System. This paper attempts to bridge this gap by
providing several statistical, machine learning, deep learning and zero-shot
learning based methods to effectively analyze legal judgments from Hong Kong's
Court System. The methods proposed consists of: (1) Citation Network Graph
Generation, (2) PageRank Algorithm, (3) Keyword Analysis and Summarization, (4)
Sentiment Polarity, and (5) Paragrah Classification, in order to be able to
extract key insights from individual as well a group of judgments together.
This would make the overall analysis of judgments in Hong Kong less tedious and
more automated in order to extract insights quickly using fast inferencing. We
also provide an analysis of our results by benchmarking our results using Large
Language Models making robust use of the HuggingFace ecosystem.

本文探讨了使用统计分析、机器学习、深度学习、零样本学习等基于自然语言处理技术的方法，以有效地分析来自香港法院系统的法律判决，并提供了五种方法，从而能够从个人以及一组判决中提取关键见解和极性 sentiment，以便快速提取洞见并使香港判决的整体分析更少烦琐、更自动化。