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| This is the official page of Nerf, a tool for Nested Named Entity Recognition based on the Conditional Random Fields modelling technique. The Nerf tool is released under the [[http://www.gnu.org/licenses/gpl.html|GNU General Public License v3]] and by downloading the Nerf package you accept the conditions of that licence. | Nerf is a statistical named entity recognition tool based on linear-chain conditional random fields. |
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| '''Author:''' Jakub Waszczuk <<MailTo(jakub DOT waszczuk AT SPAMFREE ipipan DOT waw DOT pl)>> <<BR>> '''Author:''' Michał Lenart <<MailTo(michal DOT lenart AT SPAMFREE ipipan DOT waw DOT pl)>> <<BR>> |
'''Principal developer:''' [[http://zil.ipipan.waw.pl/JakubWaszczuk|Jakub Waszczuk]] <<BR>> '''License:''' 2-clause BSD |
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| Readme file of the current version, in English [[attachment:NERF.pdf]] | See the [[https://github.com/kawu/nerf/blob/master/README.md#nerf|README]] file from the development repository. |
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| You can download the current distribution package from [[attachment:nerf.dist.0.2.tgz|here]]. The package consists of three components: | Nerf is available in a form of a software distribution which can be downloaded from [[http://hackage.haskell.org/package/nerf|Hackage]] using the [[http://www.haskell.org/cabal/|Cabal]] tool. To compile Nerf you will also need the [[http://www.haskell.org/ghc/|Glasgow Haskell Compiler]] (GHC). The simplest way to get both Cabal and GHC is to install the [[http://www.haskell.org/platform/|Haskell Platform]]. Please see the documentation for more information about the installation process. === Pre-trained model === A model for the Polish language has been trained on the [[http://clip.ipipan.waw.pl/LRT?action=AttachFile&do=view&target=NKJP-PodkorpusMilionowy-1.1.tgz|manually annotated subcorpus]] of the [[http://nkjp.pl/index.php?page=0&lang=1|National Corpus of Polish (NCP)]]. An archive file with the model can be downloaded from [[attachment:model-0.3-4.0.zip|here]]. The model can be used to recognize embedded structures of named entities consistent with the type hierarchy used in NCP. == Python version == '''The Python version of Nerf is no longer supported.''' '''Authors:''' [[http://zil.ipipan.waw.pl/JakubWaszczuk|Jakub Waszczuk]], [[http://zil.ipipan.waw.pl/MichalLenart|Michał Lenart]] <<BR>> '''License:''' GPL v.3 Readme file of the Python version, in English [[attachment:NERF.pdf]] You can download the obsolete distribution package from [[attachment:nerf.dist.0.2.tgz|here]]. The package consists of three components: |
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You can also download the newest versions of both pycrf library and Nerf tool directly from repositories: * svn co svn://chopin.ipipan.waw.pl/nkjp/pycrf/trunk pycrf * svn co svn://chopin.ipipan.waw.pl/nkjp/ner/trunk ner If you have any problems with tool installation or usage, please send report to waszczuk.kuba@gmail.com. |
Nerf
Nerf is a statistical named entity recognition tool based on linear-chain conditional random fields.
Principal developer: Jakub Waszczuk
License: 2-clause BSD
Documentation
See the README file from the development repository.
Downloads
Nerf is available in a form of a software distribution which can be downloaded from Hackage using the Cabal tool. To compile Nerf you will also need the Glasgow Haskell Compiler (GHC). The simplest way to get both Cabal and GHC is to install the Haskell Platform. Please see the documentation for more information about the installation process.
Pre-trained model
A model for the Polish language has been trained on the manually annotated subcorpus of the National Corpus of Polish (NCP). An archive file with the model can be downloaded from here. The model can be used to recognize embedded structures of named entities consistent with the type hierarchy used in NCP.
Python version
The Python version of Nerf is no longer supported.
Authors: Jakub Waszczuk, Michał Lenart
License: GPL v.3
Readme file of the Python version, in English NERF.pdf
You can download the obsolete distribution package from here. The package consists of three components:
- Python pycrf library, which has to be installed before the Nerf tool can be used,
- The Nerf tool itself,
- Supplementary data: trained models and examples of configuration files.