textkorpuses

Textkorpuses, or text corpora, are large, structured collections of natural language texts used for linguistic analysis and natural language processing. They include written materials from multiple genres and time periods and may carry metadata such as language, date, author, and genre. Many textkorpuses also include linguistic annotations—such as part-of-speech tags, lemmas, named entities, syntactic structures, or semantic roles—that enable more advanced analysis. They can be monolingual, bilingual, or multilingual, and may be general in scope or domain-specific (for example, news, legal, or medical texts). Parallel corpora pair texts with translations to study transfer or to train machine translation.

Creation and structure: textkorpuses are assembled from digitized or born-digital texts obtained under licensing that permits use for research. They are preprocessed with cleaning, normalization, and tokenization, and stored in machine-readable formats. Annotations, when present, are maintained in separate layers or formats to support querying and analysis. Metadata and provenance are documented to support reproducibility and rights management.

Access and use: textkorpuses are accessed via files or specialized search interfaces and concordancers. They support qualitative and quantitative work, including measures such as token frequency, n-grams, and collocations, as well as concordances and stylistic analysis. Common uses include linguistic research, lexicography, language teaching, and training or evaluating NLP models, such as language models and translation systems. Notable examples include the British National Corpus, the Corpus of Contemporary American English, the Leipzig Corpora Collection, Europarl, and Penn Treebank. Challenges include sampling bias, representativeness, copyright restrictions, annotation quality, and privacy concerns.