NER Tagger Model Training

This workflows shows how to train a model for named-entity recognition. The model can be created with the StanfordNLP NE Learner node which creates a conditional random field (CRF) model. To create the model, a document training set and a dictionary with known named-entities is needed. Due to generalization of word patterns, the model can be used by the tagger to find new named-entitities in unknown documents. A Scorer node for model evaluation is also available.

NER Tagger Model Training

 

Resources

EXAMPLES Server: 08_Other_Analytics_Types/01_Text_Processing/14_NER_Tagger_Model_Training08_Other_Analytics_Types/01_Text_Processing/14_NER_Tagger_Model_Training*
Download a zip-archive

 

 


* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually