This is a state of the art NER tagger that tags plain text with named entities. The newest version tags entities with either the "classic" 4-label type set (people / organizations / locations / miscellaneous), while the most recent can also tag entities with a larger 18-label type set (based on the OntoNotes corpus). It uses gazetteers extracted from Wikipedia, word class models derived from unlabeled text, and expressive non-local features. The best performance is 90.8 F1 on the CoNLL03 shared task data. The tagger is robust and has been evaluated on a variety of datasets. For detailed results, design and modeling details, please read the paper.
The NE tagger can be run as a standalone NLP tool, or as a component in the Illinois NLP Curator (http://cogcomp.cs.illinois.edu/page/software_view/Curator).