Stanford NER 3.4.1

A Conditional Random Field sequence model
Stanford NER
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Stanford NER is a free and open source utility that provides a high-performance machine learning based named entity recognition system, including facilities to train models from supervised training data and pre-trained models for English.

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.

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.

Detailed instructions on how to install and use the Stanford NER utility on your Mac are available HERE.

Stanford NER is a cross-platform utility capable of running on any operating system that comes with Java support (e.g. Mac OS X, Windows, Linux).

last updated on:
August 30th, 2014, 18:38 GMT
file size:
90.3 MB
developed by:
Stanford NLP Group
license type:
operating system(s):
Mac OS X
binary format:
Home \ Math/Scientific
Stanford NER
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Stanford NER
What's New in This Release:
  • Added Spanish models
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