The NER functions that use the Conditional Random Fields (CRF) model are:
- NERTrainer, which takes training data and outputs a CRF model (a binary file)
-
NER, which takes input documents and extracts
specified entities, using one or more CRF models and, if appropriate, rules (regular
expressions) or a dictionary
The function uses models to extract the names of persons, locations, and organizations; rules to extract entities that conform to rules (such as phone numbers, times, and dates); and a dictionary to extract known entities.
- NEREvaluator, which evaluates a CRF model
The CRF model implementation supports English, simplified Chinese, and traditional Chinese text. The maximum entropy model implementation supports only English text.
The NER functions that use the Max Entropy Model are documented in NER Functions (Maximum Entropy Model Implementation).