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From http://crm114.sourceforge.net/wiki/doku.php?id=download: CRM114 C-callable Library This is the callable library version of CRM114. It has most of the classifiers as the standalone language (with some significant improvements- one alpha tester says they saw a 10x speedup in their application). This version is LGPLed (Library GPL) so you can link it with your own code, whether open-source or proprietary. You still need TRE (on Fedora, “yum install tre-devel”). Note that with improvements come costs: libcrm114 classifiers are NOT compatible with standalone CRM114 class files (necessary, because libcrm114 classifiers can work even on systems that don't have filesystems, like embedded processors). The code is now pretty stable and the API solidly entrenched by use in several real products, so the api is unlikely to change in unpleasant ways. Advantages of libcrm114: It's much faster; everything is in-memory. You can call everything directly from ANSI C. Because everything is in memory, it's good for embedded systems where you don't _have_ a unix-style file system to talk to. No arcane language to learn, it's all just ANSI C. You can export classifiers as ASCII “CSV-like” format so trained classifiers are 32/64-bit portable and cross-platform Linux/Mac/Windows portable (the internal binary classifier format is still tied to a particular architecture, but that's never exported any more). Disadvantages of libcrm114: Not all classifiers are currently supported (in particular, Neural Net, Correllator, OSBF, and Winnow are NOT yet supported). There's no crazy language, so you need to get your data into memory on your own. You still need TRE. You do pay a (not horrible) startup cost loading a classifier from a an ASCII CSV-like file, but since you can then reuse the classifier for as many documents as you want, in the long term this cost is amortized down to zero and you get significant speedup. Dependencies Debian/Ubuntu: libtre5, libtre-dev Building $ make && cd python && python setup build