Towse: views from the hill

June 12, 2008

[URL] Corpus of American English

Filed under: app,resource,wordstuff,writing — Towse @ 12:26 am

Corpus of American English

Brilliant app.

The Corpus of American English (not to be confused with the American National Corpus) is the first large corpus of contemporary American English. It is freely available online, and it is related to other large corpora that we have created.

The corpus contains more than 360 million words of text, including 20 million words each year from 1990-2007, and it is equally divided among spoken, fiction, popular magazines, newspapers, and academic texts (more information). The corpus will also be updated at least twice each year from this point on, and will therefore serve as a unique record of linguistic changes in American English.

The interface allows you to search for exact words or phrases, wildcards, lemmas, part of speech, or any combinations of these. You can search for surrounding words (collocates) within a ten-word window (e.g. all nouns somewhere near chain, all adjectives near woman, or all verbs near key).

The corpus also allows you to easily limit searches by frequency and compare the frequency of words, phrases, and grammatical constructions, in at least two main ways:

* By genre: comparisons between spoken, fiction, popular magazines, newspapers, and academic, or even between sub-genres (or domains), such as movie scripts, sports magazines, newspaper editorial, or scientific journals
* Over time: compare different years from 1990 to the present time

You can also easily carry out semantically-based queries of the corpus. For example, you can contrast and compare the collocates of two related words (little/small, democrats/republicans, men/women), to determine the difference in meaning or use between these words. You can find the frequency and distribution of synonyms for nearly 60,000 words and also compare their frequency in different registers, and also use these word lists as part of other queries. Finally, you can easily create your own lists of semantically-related words, and then use them directly as part of the query.

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