Document Vector Hashing

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 either the relative frequency or the absolute frequency of the terms. The advantages of using this node instead of the normal document vector node is that the dimension of the vectors is always fixed and therefore this node is streamable.

Options

Document column

The column containing the documents to use.

Dimension

The dimension of the output document vector. The bigger the dimension, the less likely collisions would tend to happen. However, be aware of the curse of dimensionality.

Seed

Seed for the hashing function.

Hashing function

Choose which hashing function should be used to hash the document terms.

Vector type

There are three ways to fill the values in the document vector.
Binary : The vector will be a bit vector.
TF-Absolute : At each index where a term is hashed to, the value of the absolute term frequency of that term will calculated and stored at the index.
TF-Relative : At each index where a term is hashed to, the value of the relative term frequency of that term will calculated and stored at the index.

As collection cell

If checked all vector entries will be stored in a collection cell consisting of double cells. 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

• 03_Streaming_DocumentVectorKNIME Hub
• 13_DocumentVector_HashingKNIME Hub
• Exercise 3. DocumentVectorHashing_Creation_on_Large_Movie_Review_DatasetKNIME Hub

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