TD_​WordEmbeddings

This function generates embeddings of documents and words. It also finds similarity between the texts. This helps in machine learning applications because many machine learning algorithms process real values rather than text.

Options

Accumulate

Specify which columns to be accumulated from input to output.

ConvertToLowerCase

Specify whether to convert input data to lower case.

IDColumn

Specify the IDColumn in InputTable which contains unique values.

ModelTextColumn

Specify the ModelTable column which contains the token in ModelTable.

ModelVectorColumns

Specify the ModelTable columns which contains the vectors for each token.

Operation

Specify which operation needs to be performmed on the data.

PrimaryColumn

Specify the PrimaryColumn which contains the text data.

RemoveStopWords

Specify whether to remove stop words the input data.

SecondaryColumn

Specify the SecondaryColumn which contains the text data applicable only if operation is token2token-similarity or Doc2Doc-similarity.

StemTokens

Specify whether to stem tokens from the input data.

Output Schema

Output Schema, if Volatile is true then use user login as the schema.

Output Table

Output Table

VAL Location

VAL Location

Volatile

Specifies whether the table should be a VOLATILE table. If true, then the table is automatically deleted, otherwise it is users responsibility to remove or clean it up for space.

Nodes

• Column Filter Configuration6 ×
• Table Row to Variable6 ×
• Boolean Configuration4 ×
• String Configuration3 ×
• Python Script2 ×
• Show all 14 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Database
• FeatureKNIME Quick Forms

Links

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