NER_Tagger_Model_Training
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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-entities in unknown documents. A StanfordNLP NE Scorer node for model evaluation is also available.
URL: Slides KNIME Analytics Platform Text Mining https://www.knime.com/form/material-download-registration
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