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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.
For the moment, you can ignore the details and just concentrate on the output.
The Reuters Corpus contains 10,788 news documents totaling 1.3 million words.
: 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).
NLTK's corpus readers support efficient access to a variety of corpora, and can be used to work with new corpora.
As just mentioned, a text corpus is a large body of text.
Often there is insufficient government or industrial support for developing language resources, and individual efforts are piecemeal and hard to discover or re-use.
The graph in fig-inaugural used "word offset" as one of the axes; this is the numerical index of the word in the corpus, counting from the first word of the first address.
However, the corpus is actually a collection of 55 texts, one for each presidential address.
The documents have been classified into 90 topics, and grouped into two sets, called "training" and "test"; thus, the text with fileid Unlike the Brown Corpus, categories in the Reuters corpus overlap with each other, simply because a news story often covers multiple topics.
We can ask for the topics covered by one or more documents, or for the documents included in one or more categories.
We examined some small text collections in 1., such as the speeches known as the US Presidential Inaugural Addresses.