The NER functions that use the maximum entropy model are:
- TrainNamedEntityFinder, which takes training data and outputs a maximum entropy model
FindNamedEntity, which takes input documents and extracts specified entities, using a maximum entropy model and, if appropriate, rules (regular expressions) or a dictionary
The function uses a model to extract the entity types "person", "location", and "organization" and rules to extract the entity types "date", "time", "email" and "money". If you specify these entity names, the function invokes the default model types and model file names. To extract all entities in one FindNamedEntity call, specify "all".
- Evaluate Named Entity Finder, which evaluates a maximum entropy model model
The maximum entropy model implementation supports only English text. The CRF model implementation supports English, simplified Chinese, and traditional Chinese text.
The NER functions that use the Conditional Random Fields (CRF) Model are documented in NER Functions (CRF Model Implementation).