The statistical analysis of large scale legal corpus can provide valuable
legal insights. For such analysis one needs to (1) select a subset of the
corpus using document retrieval tools, (2) structuralize text using information
extraction (IE) systems, and (3) visualize the data for the statistical
analysis. Each process demands either specialized tools or programming skills
whereas no comprehensive unified "no-code" tools have been available.
Especially for IE, if the target information is not predefined in the ontology
of the IE system, one needs to build their own system. Here we provide NESTLE,
a no code tool for large-scale statistical analysis of legal corpus. With
NESTLE, users can search target documents, extract information, and visualize
the structured data all via the chat interface with accompanying auxiliary GUI
for the fine-level control. NESTLE consists of three main components: a search
engine, an end-to-end IE system, and a Large Language Model (LLM) that glues
the whole components together and provides the chat interface. Powered by LLM
and the end-to-end IE system, NESTLE can extract any type of information that
has not been predefined in the IE system opening up the possibility of
unlimited customizable statistical analysis of the corpus without writing a
single line of code. The use of the custom end-to-end IE system also enables
faster and low-cost IE on large scale corpus. We validate our system on 15
Korean precedent IE tasks and 3 legal text classification tasks from LEXGLUE.
The comprehensive experiments reveal NESTLE can achieve GPT-4 comparable
performance by training the internal IE module with 4 human-labeled, and 192
LLM-labeled examples. The detailed analysis provides the insight on the
trade-off between accuracy, time, and cost in building such system.

NESTLE 是一个无代码工具，用于大规模法律语料库的统计分析，其通过搜索引擎、端到端信息抽取系统和大型语言模型为用户提供文档检索、信息提取和数据可视化功能，无需编写任何代码，可以提供定制化的统计分析，提高效率和降低成本。

NESTLE：法律语料统计分析的无代码工具

NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus

This paper conducts a comparative study on the performance of various machine
learning (``ML'') approaches for classifying judgments into legal areas. Using
a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how
state-of-the-art NLP methods compare against traditional statistical models
when applied to a legal corpus that comprised few but lengthy documents. All
approaches tested, including topic model, word embedding, and language
model-based classifiers, performed well with as little as a few hundred
judgments. However, more work needs to be done to optimize state-of-the-art
methods for the legal domain.

本文研究比较了各种机器学习方法，用于将判决分为不同的法律领域，结果表明包括主题模型、词向量和语言模型分类器在内的所有方法都表现良好，但还需要进一步优化这些先进的方法以适应法律领域。

法律领域分类：新加坡最高法院裁决文本分类器的比较研究

Legal Area Classification: A Comparative Study of Text Classifiers on  Singapore Supreme Court Judgments

We compare policy differences across institutions by embedding
representations of the entire legal corpus of each institution and the
vocabulary shared across all corpora into a continuous vector space. We apply
our method, Gov2Vec, to Supreme Court opinions, Presidential actions, and
official summaries of Congressional bills. The model discerns meaningful
differences between government branches. We also learn representations for more
fine-grained word sources: individual Presidents and (2-year) Congresses. The
similarities between learned representations of Congresses over time and
sitting Presidents are negatively correlated with the bill veto rate, and the
temporal ordering of Presidents and Congresses was implicitly learned from only
text. With the resulting vectors we answer questions such as: how does Obama
and the 113th House differ in addressing climate change and how does this vary
from environmental or economic perspectives? Our work illustrates
vector-arithmetic-based investigations of complex relationships between word
sources based on their texts. We are extending this to create a more
comprehensive legal semantic map.

使用唯一向量空间嵌入法比较不同机构之间的政策差异，发现各机构之间存在有意义的差异，并使用文法向量回答具体问题，正在扩展为更综合的法律语义地图。