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20 lines
1 KiB
Text
20 lines
1 KiB
Text
Libtextcat is a library with functions that implement the
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classification technique described in Cavnar & Trenkle, "N-Gram-Based
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Text Categorization". It was primarily developed for language
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guessing, a task on which it is known to perform with near-perfect
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accuracy.
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The central idea of the Cavnar & Trenkle technique is to calculate a
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"fingerprint" of a document with an unknown category, and compare this
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with the fingerprints of a number of documents of which the categories
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are known. The categories of the closest matches are output as the
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classification. A fingerprint is a list of the most frequent n-grams
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occurring in a document, ordered by frequency. Fingerprints are
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compared with a simple out-of-place metric. See the article for more
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details.
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Considerable effort went into making this implementation fast and
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efficient. The language guesser processes over 100 documents/second on
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a simple PC, which makes it practical for many uses. It was developed
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for use in our webcrawler and search engine software, in which it it
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handles millions of documents a day.
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