The nerf package
The package provides the named entity recognition (NER) tool divided into a back-end library (see the NLP.Nerf module) and the front-end tool nerf. Using the library you can model and recognize named entities (NEs) which, for a particular sentence, take the form of forest with NE category values kept in internal nodes and sentence words kept in forest leaves.
To model NE forests we combine two different techniques. The IOB codec is used to translate to and fro between the original, forest representation of NEs and the sequence of atomic labels. In other words, it provides two isomorphic functions for encoding and decoding between both representations. Linear-chain conditional random fields, on the other hand, provide the framework for label modelling and tagging.
|Versions||0.1.0, 0.2.0, 0.2.1, 0.2.2, 0.3.0, 0.4.0, 0.5.0, 0.5.1, 0.5.2, 0.5.3|
|Dependencies||base (==4.*), binary, cmdargs, containers, crf-chain1 (==0.2.*), data-named (==0.5.*), dawg (==0.5.*), monad-ox (==0.2.*), polimorf (>=0.5.0 && <0.6), polysoup (==0.1.*), sgd (>=0.2.1 && <0.3), text, text-binary (==0.1.*), vector [details]|
|Copyright||Copyright (c) 2012 IPI PAN|
|Category||Natural Language Processing|
|Source repository||head: git clone https://github.com/kawu/nerf.git|
|Uploaded||Fri Nov 23 09:50:31 UTC 2012 by JakubWaszczuk|
|Downloads||1853 total (22 in the last 30 days)|
|Status||Docs uploaded by user
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