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Stanford Named Entity Recognizer 1.2.4

Company: Stanford NLP Group
Date Added: June 24, 2013  |  Visits: 208

Stanford Named Entity Recognizer

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Stanford NER (also known as CRFClassifier) is a Java implementation of a Named Entity Recognizer. Named Entity Recognition (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names. The software provides a general (arbitrary order) implementation of linear chain Conditional Random Field (CRF) sequence models, coupled with well-engineered feature extractors for Named Entity Recognition. (CRF models were pioneered by Lafferty, McCallum, and Pereira (2001); see Sutton and McCallum (2006) for a better introduction.) Included with the download are good 3 class (PERSON, ORGANIZATION, LOCATION) named entity recognizers for English (in versions with and without additional distributional similarity features) and another pair of models trained on the CoNLL 2003 English training data. The distributional similarity features improve performance but the models require considerably more memory.<br /><br />The CRF code is by Jenny Finkel. The feature extractors are by Dan Klein, Christopher Manning, and Jenny Finkel. Much of the documentation and usability is due to Anna Rafferty. The CRF sequence models provided here do not precisely correspond to any published paper, but the correct paper to cite for the software is:<br /><br /> Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363-370. http://nlp.stanford.edu/~manning/papers/gibbscrf3.pdf <br /><br />The software provided here is similar to the baseline local+Viterbi model in that paper, but adds new distributional similarity based features (in the -distSim classifiers). The big models were trained on a mixture of CoNLL, MUC-6, MUC-7 and ACE named entity corpora, and as a result the models are fairly robust across domains.<br /><br />You can look at a Powerpoint Introduction to NER and the Stanford NER package [ppt] [pdf] or the FAQ, which has some information on training models. Further documentation is provided in the included README and in the javadocs.<br /><br />Stanford NER is available for download, licensed under the GNU General Public License (v2 or later). Source is included. The package includes components for command-line invocation, running as a server, and a Java API. Stanford NER code is dual licensed (in a similar manner to MySQL, etc.). Open source licensing is under the full GPL, which allows many free uses. For distributors of proprietary software, commercial licensing with a ready-to-sign agreement is available. If you don't need a commercial license, but would like to support maintenance of these tools, we welcome gift funding.

Requirements: No special requirements
Platforms: Mac, Mac OSX
Keyword: Based Baseline Classifiers Corpora Distsim Local Bviterbi Mixture Model Result Similar
Users rating: 0/10

License: Shareware Size: 59.59 MB
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