minicorpus

A minicorpus is a small, representative subset of a larger text corpus designed for rapid experimentation, teaching, and benchmarking. It aims to preserve essential linguistic properties and thematic coverage of the full collection while remaining manageable in size for quick processing.

Size and scope characteristics vary by field, but a minicorpus typically contains tens of thousands to a few hundred thousand words. It is often constructed to reflect a defined mix of genres, domains, or time periods to retain relevant variation. Carefully selected samples help ensure that the minicorpus remains informative without becoming biased toward a single source. Preprocessing steps such as normalization, tokenization, and metadata tagging are commonly performed, and licensing and privacy considerations are observed to ensure lawful use.

Construction and use: Define the research or teaching objective, select candidate sources, sample texts, and remove duplicates. Standard preprocessing and documentation of methods are recommended. A minicorpus is frequently used for quick prototyping of NLP models, feature engineering, or baseline comparisons, as well as for educational demonstrations and small-scale linguistic analyses. It provides a low-cost environment to iterate before scaling to the full corpus or multiple datasets.

Limitations and caveats: Because of its small size, a minicorpus may introduce sampling bias and may not capture the full diversity of the larger collection. Results derived from it should be treated as indicative rather than definitive and validated on larger datasets. Clear documentation of scope, sampling strategy, size, sources, and licensing is advised, along with a plan to expand to a full corpus when appropriate.