Difference between revisions of "Named entity recognizers"
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| − | * [http://www.aueb.gr/users/ion/software/GREEK_NERC_v2.tar.gz Greek named entity recognizer (version 2)] | + | * [http://www.aueb.gr/users/ion/software/GREEK_NERC_v2.tar.gz Greek named entity recognizer (version 2)] It currently identifies temporal expressions, person names, and organization names; see [http://www.aueb.gr/users/ion/publications.html here] for publications describing the recognizer. |
*[http://balie.sourceforge.net/ Balie] Baseline implementation of named entity recognition. | *[http://balie.sourceforge.net/ Balie] Baseline implementation of named entity recognition. | ||
*[http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=FLBJNE UIUC NER] Java-based UIUC NER tagger. Uses gazetteers extracted from Wikipedia, word-class model built from unlabeled text and extensively uses non-local features. Achieves 90.8F1 score on the CoNLL03 shared task data and is robust on other datasets. Try the [http://l2r.cs.uiuc.edu/~cogcomp/LbjNer.php LBJ-NER-Demo] | *[http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=FLBJNE UIUC NER] Java-based UIUC NER tagger. Uses gazetteers extracted from Wikipedia, word-class model built from unlabeled text and extensively uses non-local features. Achieves 90.8F1 score on the CoNLL03 shared task data and is robust on other datasets. Try the [http://l2r.cs.uiuc.edu/~cogcomp/LbjNer.php LBJ-NER-Demo] | ||
Revision as of 15:20, 29 January 2009
Software - Named entity recognizers
- Greek named entity recognizer (version 2) It currently identifies temporal expressions, person names, and organization names; see here for publications describing the recognizer.
- Balie Baseline implementation of named entity recognition.
- UIUC NER Java-based UIUC NER tagger. Uses gazetteers extracted from Wikipedia, word-class model built from unlabeled text and extensively uses non-local features. Achieves 90.8F1 score on the CoNLL03 shared task data and is robust on other datasets. Try the LBJ-NER-Demo
- Older version of UIUC NER - identifies/classifies entities as Person, Location, Organization and Misc (this last category relates to languages and nationalities); fast and robust; try the demo
- Stanford NER Conditional Random Fields based NER. Also incorporates distributional similarity based features extracted from the English Gigaword corpus.