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analysis. It can handle the classification of, for example, titles,
questions, sentences, and short messages. Main features of
LibShortText include
* It is more efficient than general text-mining packages. On a
typical computer, processing and training 10 million short texts
takes only around half an hour.
* The fast training and testing is built upon the linear classifier
* LIBLINEAR
* Default options often work well without tedious tuning.
* An interactive tool for error analysis is included. Based on the
property that each short text contains few words, LibShortText
provides details in predicting each text.
6 lines
329 B
Text
6 lines
329 B
Text
$NetBSD: distinfo,v 1.1.1.1 2014/10/29 17:06:40 cheusov Exp $
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SHA1 (libshorttext-1.1.tar.gz) = 2d9705195682fa1f25de30bd66711685f974a8c0
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RMD160 (libshorttext-1.1.tar.gz) = 569d2f2a64f8fc311766b08cbef7086e1340ce55
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Size (libshorttext-1.1.tar.gz) = 817775 bytes
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SHA1 (patch-text-train.py) = 6b99bdfcfd69a9d7e83cafc852202dc23485dfeb
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