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04_​Using_​Jupyter_​from_​KNIME_​to_​embed_​documents

Workflow

This workflow uses functionality provided in a Jupyter notebook to embed documents from a topic-space representation into2D Euclidean space. The embedding is done using scikit-learn's implementation of the t-SNE algorithm.KNIME Extensions requirements: - Textprocessing - KNIME Python ScriptingThe Python Scripting integration needs to be configured to use a Python environment with scikit-learn and jupyter installed. Load data and build topic model Visualize results t-SNE embedding Embed using tSNERead documents Python Script (1⇒1) Topic Extractor(Parallel LDA) Create CollectionColumn Color Manager ReferenceRow Filter Joiner Interactivevisualization Table Reader Pick 10 randomqueries Preprocessdocuments This workflow uses functionality provided in a Jupyter notebook to embed documents from a topic-space representation into2D Euclidean space. The embedding is done using scikit-learn's implementation of the t-SNE algorithm.KNIME Extensions requirements: - Textprocessing - KNIME Python ScriptingThe Python Scripting integration needs to be configured to use a Python environment with scikit-learn and jupyter installed. Load data and build topic model Visualize results t-SNE embedding Embed using tSNERead documentsPython Script (1⇒1) Topic Extractor(Parallel LDA) Create CollectionColumn Color Manager ReferenceRow Filter Joiner Interactivevisualization Table Reader Pick 10 randomqueries Preprocessdocuments

Download

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Nodes

04_​Using_​Jupyter_​from_​KNIME_​to_​embed_​documents consists of the following 20 nodes(s):

Plugins

04_​Using_​Jupyter_​from_​KNIME_​to_​embed_​documents contains nodes provided by the following 4 plugin(s):