creates a document vector for each document representing
terms space, exactly as the normal document vector node. The
difference is that this node takes one data table and one Document Vector model as input:
1. Document Vector model containing names of feature space columns and node settings
2. Table containing the bag-of-words terms
The terms from the first input will be converted into document vectors using the vector from the second input as the reference. Features that appear in first table, but not in the model input will be filtered out, and features that appear in model input, but not in the first table will be added to the output vector and their values will be set to 0.
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.
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