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This chapter continues to present programming concepts by example, in the context of a linguistic processing task.

We will wait until later before exploring each Python construct systematically.

An interesting property of this collection is its time dimension: Many text corpora contain linguistic annotations, representing POS tags, named entities, syntactic structures, semantic roles, and so forth.

NLTK provides convenient ways to access several of these corpora, and has data packages containing corpora and corpus samples, freely downloadable for use in teaching and research. For information about downloading them, see : Cumulative Word Length Distributions: Six translations of the Universal Declaration of Human Rights are processed; this graph shows that words having 5 or fewer letters account for about 80% of Ibibio text, 60% of German text, and 25% of Inuktitut text.

: Common Structures for Text Corpora: The simplest kind of corpus is a collection of isolated texts with no particular organization; some corpora are structured into categories like genre (Brown Corpus); some categorizations overlap, such as topic categories (Reuters Corpus); other corpora represent language use over time (Inaugural Address Corpus).

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(See 7 for suggestions on how to locate language resources.) We have seen a variety of corpus structures so far; these are summarized in 1.3.

The filename contains the date, chatroom, and number of posts; e.g., The Brown Corpus was the first million-word electronic corpus of English, created in 1961 at Brown University.

This corpus contains text from 500 sources, and the sources have been categorized by genre, such as Next, we need to obtain counts for each genre of interest.

The simplest kind lacks any structure: it is just a collection of texts.

Often, texts are grouped into categories that might correspond to genre, source, author, language, etc.Sometimes these categories overlap, notably in the case of topical categories as a text can be relevant to more than one topic.