A Corpus-Based Judicial Vocabulary List

Guangquan Hu, Jinying Guo and Lei Liu

SAGE Open, 2025, vol. 15, issue 3, 21582440251374486

Abstract: This study develops a Judicial Vocabulary List (JVL) based on the United States Supreme Court Decision Corpus (SCDC) from 1999 to 2023, addressing the lack of specialized vocabulary resources for legal English. Using Python, we analyzed the SCDC through statistical measures such as normalized frequency, range ratio, dispersion, and frequency ratio to identify high-frequency lemmas. The final JVL comprises 943 lemmas, primarily nouns, verbs, adjectives, and adverbs, covering 22.15% of Supreme Court decision texts. These vocabulary items are essential for understanding American case law. Data collection involved downloading Supreme Court decisions from the official website and cross-referencing them with the LexisNexis database to ensure accuracy. Text processing and cleaning were performed using Python libraries, and the corpus was analyzed using the Spacy library for lemmatization and part-of-speech tagging. Harvard Law Corpus (HLC) and Black’s Law Dictionary served as a reference to validate the relevance of JVL. The JVL is designed to assist legal English learners, particularly non-native English speakers, in mastering core legal concepts. It also provides educators with a resource to design targeted teaching materials and aids researchers in categorizing and analyzing Supreme Court decisions by legal domain or specific issues. The findings underscore the importance of domain-specific vocabulary in legal education and practice, offering a valuable tool for legal professionals and scholars to study Supreme Court decisions effectively. This research contributes to legal linguistics by providing a detailed, corpus-based vocabulary list that reflects the unique lexical features of Supreme Court decisions.

Keywords: vocabulary list; Supreme Court decisions; judicial corpus; English for Legal Purposes; lexical learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251374486

DOI: 10.1177/21582440251374486

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