[1] Corpora are balanced, often stratified collections of authentic, "real world", text of speech or writing that aim to represent a given linguistic variety.
Corpus linguistics proposes that a reliable analysis of a language is more feasible with corpora collected in the field—the natural context ("realia") of that language—with minimal experimental interference.
In the Western European tradition, scholars prepared concordances to allow detailed study of the language of the Bible and other canonical texts.
[6] Kučera and Francis subjected the Brown Corpus to a variety of computational analyses and then combined elements of linguistics, language teaching, psychology, statistics, and sociology to create a rich and variegated opus.
Other corpora represent many languages, varieties and modes, and include the International Corpus of English, and the British National Corpus, a 100 million word collection of a range of spoken and written texts, created in the 1990s by a consortium of publishers, universities (Oxford and Lancaster) and the British Library.
[11] In the 1990s, many of the notable early successes on statistical methods in natural-language programming (NLP) occurred in the field of machine translation, due especially to work at IBM Research.
An example is the Andersen-Forbes database of the Hebrew Bible, developed since the 1970s, in which every clause is parsed using graphs representing up to seven levels of syntax, and every segment tagged with seven fields of information.
This is a recent project with multiple layers of annotation including morphological segmentation, part-of-speech tagging, and syntactic analysis using dependency grammar.
[20] Corpus linguistics has generated a number of research methods, which attempt to trace a path from data to theory.
However even corpus linguists who work with 'unannotated plain text' inevitably apply some method to isolate salient terms.