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StanfordNLP NE Scorer

KNIME Textprocessing Plug-in version 4.2.1.v202008251908 by KNIME AG, Zurich, Switzerland

This nodes calculates some quality measures like precision, recall and f1-measures and counts the amount of true positives, false negatives and false positives to validate a Stanford NLP NE model. Internally the node tags the incoming test document set with a dictionary tagger which is based on the dictionary which was used for tagging the training set in the learner node. After tagging the documents, the input model tags the documents again and the node calculates the differences between the tags created by the dictionary tagger and the tags created by the input model.

Options

Document Column
The document column containing the test data set.

Input Ports

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The input table containing the test document data set.
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The input port object containing the StanfordNLP NE model, the used dictionary and the used tag.

Output Ports

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The table containing the validation scores.

Best Friends (Incoming)

Best Friends (Outgoing)

Workflows

Installation

To use this node in KNIME, install KNIME Textprocessing from the following update site:

KNIME 4.2

A zipped version of the software site can be downloaded here.

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Developers

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