Document Vector

This node creates a document vector for each document representing it in the terms space. The values of the feature vectors can be specified as boolean values or as values of a specified column i.e. an tf*idf column. The dimension of the vectors will be the number of distinct terms in the BoW.

Options

Document column

The column containing the documents to use.

Ignore tags

If checked tags are ignored when comparing terms.

Bitvector

If checked a bitvector will be created indicating whether a certain term is contained in a document or not.

Vector value

If Bitvector setting is not checked it is possible to specify the column to use as feature vector values. The column can i.e. contain tf*idf values which are than used as values of the feature vector. Be aware that you have to compute these values before using this node. To do so i.e. the frequency calculation nodes can be used.

As collection cell

If checked all vector entries will be stored in a collection cell consisting of double cells. The cells are ordered, the ordering is specified in the data table spec. If not checked all double cells will be stored in corresponding columns. The advantage of the column representation is that most of the regular algorithms in KNIME can be applied. The disadvantage is (which is on the other hand the advantage of the collection representation) that processing of subsequent nodes will be slowed down, due to the many columns that will be created (dependent on the input data of course).

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Workflows

• 00_Guided_Labeling_for_Document_Classification_(legacy)KNIME Hub
• 01_Document_clusteringKNIME Hub
• 01_Document_clusteringKNIME Hub
• 01_Document_clusteringKNIME Hub
• 01_Guided_Labeling_for_Document_ClassificationKNIME Hub
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