Corpusinformed

Corpusinformed describes a methodology or stance in linguistics and related fields that uses empirical corpus data to inform analysis, description, or practice. The term signals that evidence from language corpora is incorporated to shape conclusions, materials, and theories, rather than relying solely on intuition or preexisting assumptions.

In contrast to corpus-driven approaches, where theory is derived primarily from large datasets, and corpus-based work, which tests preconceived hypotheses against corpus evidence, corpusinformed methods integrate corpus data with existing theoretical frameworks and qualitative reasoning. They treat corpora as an important source of evidence that complements other data and insights, rather than as the sole basis for claims.

Applications of corpusinformed work appear across several domains, including grammar description, lexicography, language teaching, and translation studies. Practically, researchers and educators examine frequency profiles, collocations, multiword expressions, and register variation to refine descriptions, dictionaries, pedagogical materials, and translation guidelines.

Advantages of a corpusinformed approach include empirical grounding, better alignment with real-world language use, and the ability to uncover patterns not evident from intuition alone. Limitations involve potential biases in corpus composition, annotation inconsistencies, and overreliance on frequency data without sufficient contextual analysis. Effective use requires methodological transparency, careful consideration of corpus design, and integration with theoretical goals.

See also: corpus linguistics, corpus-driven, corpus-based.