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VMAWL · 835 P-lemmas · 10 frequency bands · VMC corpus evidence
Generating exercises from the corpus…
New word set every session · All 4 parts of speech · Corpus-grounded feedback
Paste up to 500 words · grammar corrections · corpus-based suggestions · VMC exemplars
Paste or upload your text on the left, select a focus area, then click Analyse text.
Analysing against the VMC corpus…
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A corpus-based academic writing assistant for veterinary medicine researchers.
VetLingua is a free AI-powered language tool built on a specialised corpus of published veterinary research articles. It is designed to help researchers explore and understand the linguistic conventions of academic writing in veterinary medicine — not just what to write, but how professionals in the field write.
Unlike general writing assistants, VetLingua is grounded in real corpus data. Every response is informed by the patterns, vocabulary, hedging strategies, reporting verbs, and rhetorical structures found in actual peer-reviewed veterinary research.
The Veterinary Medicine Corpus (VMC) is a machine-generated collection of high-quality, open-access research articles published between 2010 and 2022. Compiled using AntCorGen via the PLOS API, it captures complete articles with structural consistency across five sections: abstract, introduction, materials and methodology, results and discussion, and conclusion.
The VMC comprises 1,449 articles and 7,962,021 tokens, organised into 4 parent categories and 17 child categories with expert guidance to ensure scientifically meaningful classification:
Once you create an account, VetLingua records the interactions you have with the tool — the queries you ask, the AI's responses, your vocabulary-exercise answers, and the text you submit for writing feedback (together with the corrected/improved version). It also records lightweight usage signals: which module you're in, how long you spend there, and which language and role you're using. Your account also stores the country, preferred language, role, and optional native-language you gave at sign-up.
This data is used, in de-identified/aggregated form, for an academic research study on veterinary English learning. It also lets VetLingua tailor its responses to your language and role. It is not sold, and it is not used to advertise to you.
You choose to take part when you check the consent box at sign-up, and you can change your mind at any time. From Settings you can download a copy of everything recorded under your account, or permanently delete your account and all associated data — this cannot be undone. If you are in the European Union (including Spain), this reflects the access, portability and erasure rights you have under the GDPR.
Use the Contact us form for any question about how your data is used.
Dr. Mustafa Özer is a lecturer and researcher at the School of Foreign Languages, Abdullah Gül University. His research sits at the intersection of corpus linguistics, English for Specific Purposes (ESP), data-driven learning (DDL), and academic writing — with a particular focus on the language of veterinary medicine.
VetLingua grew directly out of the corpus Dr. Özer compiled for his PhD thesis — a carefully structured collection of peer-reviewed veterinary research articles spanning 17 topic areas. The compilation methodology and the Veterinary Medicine Academic Word List derived from it are documented in a peer-reviewed article published in English for Specific Purposes (2024).
By making this corpus accessible through an AI-powered interface, Dr. Özer aims to bridge the gap between corpus linguistics research and the practical writing needs of veterinary researchers worldwide. He has been an English Language instructor since 2005 and serves as an Associate Editor at FOCUS on ELT Journal.
Enter a word above to see corpus lines
| # | Word | PoS | Band | Frequency tier | Word family (click to explore) |
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