Developing a Corpus-Based Immigration Keyword and Key Cluster List for ESP Settings - Pitakpong, Tharin; Phoocharoensil, Supakorn; Laosrirattanachai, Piyapong (Journal of Studies in the English Language, 2026)
Vocabulary knowledge plays a central role in English for Specific Purposes, yet immigration remains a professional domain that has received little attention in specialised word list research. This study developed a corpus-based Immigration Keyword and Key Cluster List (IKKCL) to support ESP learners working with immigration forms. Data were collected from 1,670 official immigration forms downloaded from government websites in different countries. Of these, 1,500 forms formed the primary Global Immigration Forms Corpus (2,939,837 tokens), and the remaining 170 formed a secondary validation corpus (179,897 tokens). Candidate items were extracted through keyword analysis, applying Log-likelihood, Log Ratio, and Simple Maths together against the British National Corpus, followed by lexical profiling and, for two-word clusters, compounding identification and Mutual Information filtering. Surviving candidates were then validated against five institutional immigration glossaries and, where needed, examined through expert concordance analysis before being classified into technical, semi-technical, and supportive profiles. The final IKKCL contained 589 items, including 21 technical, 196 semi-technical, and 372 supportive words and clusters. The list covered an average of 19.06% of running words across both corpora, with near-identical coverage on the unseen validation corpus, indicating stable performance within the same sampling frame. Combined with the General Service List and the Academic Word List, total coverage reached approximately 90%, falling short of the threshold typically associated with independent reading comprehension. The IKKCL can be used as a resource for teaching and learning in courses such as English for Immigration and Tourism, as well as related curricula, through approaches such as data-driven learning.