RegexNER Implements a simple, rule-based NER system over token sequences using an extension of Java regular expressions. The original goal of this Annotator was to provide a simple framework to incorporate named entities and named entity labels that are not annotated in traditional NER corpora, and hence not recoginized by our statistical NER classifiers. However, you can also use this annotator to simply do rule-based NER. Here is a simple example of how to use RegexNER. RegexNER is implemented using TokensRegex. For more complex applications, you might consider using TokensRegex directly.

For English, we distribute CoreNLP with two files containing a default list of regular expressions, many just gazette entries, which label more fine-grained LOCATION-related subcategories (COUNTRY, STATE_OR_PROVINCE, CITY, NATIONALITY), the commonest online identifiers (URL, EMAIL), and a few miscellaneous categories originating from the TAC KBP evaluations (TITLE, IDEOLOGY, RELIGION, CRIMINAL_CHARGE, CAUSE_OF_DEATH). Here, TITLE refers to job titles.

Property name Annotator class name Generated Annotation
regexner RegexNERAnnotator NamedEntityTagAnnotation


Option name Type Default Description
regexner.ignorecase boolean false If true, case is ignored for all patterns in all files.
regexner.mapping String edu/stanford/nlp/models/regexner/type_map_clean Comma separated list of mapping files to use. Each mapping file is a tab-delimited file.
regexner.mapping.header String pattern,ner,overwrite,priority,group Comma separated list of header fields (or true if header is specified in the mapping file).
regexner.mapping.field.<fieldname> String null Class name for CoreLabel key for annotation fields other than NER.
regexner.commonWords String null Comma separated list of files for common words to not annotate (in case your mapping isn’t very clean).
regexner.backgroundSymbol String O,MISC Comma separated list of NER labels that can always be replaced.
regexner.posmatchtype enum MATCH_AT_LEAST_ONE_TOKEN How should validpospattern be used to match the POS of the tokens.
MATCH_ALL_TOKENS - All tokens have to match.
MATCH_AT_LEAST_ONE_TOKEN - At least one token has to match.
MATCH_ONE_TOKEN_PHRASE_ONLY - Only has to match for one token phrases.
regexner.validpospattern regex null Regular expression pattern for matching POS tags.
regexner.noDefaultOverwriteLabels String null Comma-separated list of output types for which default NER labels are not overwritten. For these types, only if the matched expression has an NER type matching the specified overwriteableType for the regex will the NER type be overwritten.
regexner.verbose boolean false If true, turns on extra debugging messages.

Mapping files

The mapping file is a tab-delimited file.

The format of the default mapping file used by RegexNER is described in more detail on the Stanford NLP website.

The format and the output fields can be changed by specifying a different regexner.mapping.header.

For instance, if you wanted to mark “Stanford University” as having NER tag of “SCHOOL” and linked to “”, you can do so by adding normalized to the headers:

# Not needed, but illustrate how to link a field to an annotation

Then you would have in your mapping file have entries such as:

Stanford University\tSCHOOL\t

(In the above example, \t is used to indicate where a tab should occur.)

Note that the pattern field can be either

  • a sequence of regex over the token text, each separated by whitespace (matching “\s+”).
    Example: University of .*\tSCHOOL


  • a TokensRegex expression (marked by starting with “( “ and ending with “ )”.
    Example: ( /University/ /of/ [ {ner:LOCATION} ] )\tSCHOOL

    Using TokensRegex patterns allows for matching on other annotated fields such as POS or NER.