crf-chain1: First-order, linear-chain conditional random fields
The library provides efficient implementation of the first-order, linear-chain conditional random fields (CRFs).
Important feature of the implemented flavour of CRFs is that transition features which are not included in the CRF model are considered to have probability of 0. It is particularly useful when the training material determines the set of possible label transitions (e.g. when using the IOB encoding method). Furthermore, this design decision makes the implementation much faster for sparse datasets.
|Versions [RSS] [faq]||0.2.0, 0.2.1, 0.2.2, 0.2.3|
|Dependencies||array, base (>=4 && <4.8), binary, containers, data-lens, logfloat, monad-codec (==0.2.*), parallel, random, sgd (>=0.2.1 && <0.3), vector, vector-binary (==0.1.*), vector-th-unbox (>=0.2.1 && <0.3) [details]|
|Copyright||Copyright (c) 2012 IPI PAN|
|Revised||Revision 1 made by AdamBergmark at 2015-10-02T13:56:25Z|
|Source repo||head: git clone git://github.com/kawu/crf-chain1.git|
|Uploaded||by JakubWaszczuk at 2014-06-19T10:28:06Z|
|Downloads||3187 total (17 in the last 30 days)|
|Rating||(no votes yet) [estimated by Bayesian average]|
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