Recognizes named (PERSON, LOCATION, ORGANIZATION, MISC), numerical (MONEY, NUMBER, ORDINAL, PERCENT), and temporal (DATE, TIME, DURATION, SET) entities. Named entities are recognized using a combination of three CRF sequence taggers trained on various corpora, such as ACE and MUC. Numerical entities are recognized using a rule-based system. Numerical entities that require normalization, e.g., dates, are normalized to NormalizedNamedEntityTagAnnotation.

Property name Annotator class name Generated Annotation
ner NERClassifierCombiner NamedEntityTagAnnotation and NormalizedNamedEntityTagAnnotation


  • ner.useSUTime: Whether or not to use sutime. On by default in the version which includes sutime, off by default in the version that doesn’t. If not processing English, make sure to set this to false.
  • ner.model: NER model(s) in a comma separated list to use instead of the default models. By default, the models used will be the 3class, 7class, and MISCclass models, in that order.
  • ner.applyNumericClassifiers: Whether or not to use numeric classifiers, including SUTime. These are hardcoded for English, so if using a different language, this should be set to false.
  • sutime.markTimeRanges: Tells sutime to mark phrases such as “From January to March” instead of marking “January” and “March” separately
  • sutime.includeRange: If marking time ranges, set the time range in the TIMEX output from sutime


StanfordCoreNLP includes SUTime, Stanford’s temporal expression recognizer. SUTime is transparently called from the “ner” annotator, so no configuration is necessary. Furthermore, the “cleanxml” annotator now extracts the reference date for a given XML document, so relative dates, e.g., “yesterday”, are transparently normalized with no configuration necessary.

SUTime supports the same annotations as before, i.e., NamedEntityTagAnnotation is set with the label of the numeric entity (DATE, TIME, DURATION, MONEY, PERCENT, or NUMBER) and NormalizedNamedEntityTagAnnotation is set to the value of the normalized temporal expression. Note that NormalizedNamedEntityTagAnnotation now follows the TIMEX3 standard, rather than Stanford’s internal representation, e.g., “2010-01-01” for the string “January 1, 2010”, rather than “20100101”.

Also, SUTime now sets the TimexAnnotation key to an edu.stanford.nlp.time.Timex object, which contains the complete list of TIMEX3 fields for the corresponding expressions, such as “val”, “alt_val”, “type”, “tid”. This might be useful to developers interested in recovering complete TIMEX3 expressions.

Reference dates are by default extracted from the “datetime” and “date” tags in an xml document. To set a different set of tags to use, use the clean.datetags property. When using the API, reference dates can be added to an Annotation via edu.stanford.nlp.ling.CoreAnnotations.DocDateAnnotation, although note that when processing an xml document, the cleanxml annotator will overwrite the DocDateAnnotation if “datetime” or “date” are specified in the document.

Caseless models

It is possible to run StanfordCoreNLP with NER models that ignore capitalization. We have trained models like this for English. You can find details on the Caseless models page.

More information

For more details on the CRF tagger see this page.