CoreNLP NER

Supported Labeling Types: Span Labeling

CoreNLP Named Entity Recognition (NER) is a feature of the Stanford CoreNLP toolkit that identifies and classifies named entities in text, such as people, organizations, locations, dates, and more. It uses statistical models and linguistic rules to extract structured information from unstructured text. In our labeling platform, CoreNLP NER can be used to automatically suggest entity labels, accelerating the annotation process and improving consistency across datasets. It supports multiple languages and is particularly useful for high-quality NER in formal or well-structured text.

ML Assisted with CoreNLP NER

Model Details

• CoreNLP NER-tagging is conducted using CoreNLP Server, leveraging the official pre-trained models.

• Utilizes nltk.parse.corenlp.CoreNLPParser as the interface to the CoreNLP server, providing a standardized access method to Stanford's advanced NLP models.

• Operates as a service within the Datasaur Intelligence container, maintaining isolation while providing consistent access.

Usage

• CoreNLP NER is best used when high-quality entity recognition is needed for tasks such as information extraction, document annotation, and search engines.

• Tagset: PERSON, LOCATION, ORGANIZATION, MISC, MONEY, NUMBER, ORDINAL, PERCENT, DATE, TIME, DURATION, SET, EMAIL, URL, CITY, STATE_OR_PROVINCE, COUNTRY, NATIONALITY, RELIGION, TITLE, IDEOLOGY, CRIMINAL_CHARGE, CAUSE_OF_DEATH, HANDLE

References

• https://stanfordnlp.github.io/CoreNLP/ner.html

• https://stanfordnlp.github.io/CoreNLP/corenlp-server.html

• https://www.nltk.org/api/nltk.parse.html#module-nltk.parse.corenlp