NER Functions (CRF Model Implementation) - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Language
English (United States)
Last Update
2018-04-17
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uce1497542673292.ditamap
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B700-1022
Product Category
Software

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).