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Overview
This Snap vectorizes sentences into a set of numeric fields. It takes two inputs:
- An array of tokenized words drawn from a set of input sentences. If your sentences are not already tokenized, use the Tokenizer Snap.
A document containing the most common words in the training set. You can generate this document using the Common Words Snap.
It then processes the data received and outputs a count frequency of the most common words in the text in each sentence.
For example: in the first output, say we have an array containing all the tokens in each sentence in a dataset; and the frequency of the 100 most common words in the second input. The Bag of Words Snap enables you to list out the number of times each of the 100 most common words appear in each input sentence.
Expected Input and Output
Expected input:
- First input view: An array of tokenized words drawn from a set of input sentences. If your sentences are not already tokenized, use the Tokenizer Snap.
- Second input view: A document containing the most common words in a given dataset, together with their frequency. You can generate this document using the Common Words Snap.
Expected output: A document containing the count frequency of the most common words in the text field.
Expected upstream Snaps:
- First input view: Any Snap that offers documents. For example: Tokenizer or Mapper.
- Second input view: Any Snap that offers documents. For example: Common Words, or a combination of File Reader and JSON Parser Snaps.
Expected downstream Snaps: Any Snap that accepts documents. For example: Cross Validator, Trainer, or a combination of JSON Formatter and File Writer Snaps.
Prerequisites
None.
Configuring Accounts
Accounts are not used with this Snap.
Configuring Views
Input | This Snap has exactly two document input views. |
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| Output | This Snap has exactly one document output view. |
| Error | This Snap has at most one document error view. |
Troubleshooting
None.
Limitations and Known Issues
None.
Modes
- Ultra Pipelines: Works in Ultra Pipelines.
Snap Settings
| Label | Required. The name for the Snap. Modify this to be more specific, especially if there are more than one of the same Snap in the pipeline. | ||||||||||||
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| Token array field | Required. The field containing the array of tokens on which you want to apply the Bag of Words technique. This suggestible property is a JSONPath. Default value: None Example: $text | ||||||||||||
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Examples
Identifying the Occurrence Frequency of the Top-100 Common Words in a Tokenized Input Dataset
In this example, we demonstrate how you can use the Bag of Words Snap to capture the frequency with which members of a set of common words appear in a dataset of tokenized sentences.
Download this pipeline.
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This pipeline contains the following Snaps:
The JSON Generator Snap contains an array of tokens created using an extract of the Yelp dataset (you can review the entire dataset here) and makes it available to the Mapper Snap as a document. If you do not have an array of tokens, you can create one using the Tokenizer Snap.
The Mapper Snap picks up the tokenized data in the input file coming from the JSON Generator Snap and maps it to the field $text, making all the relevant data accessible to the Bag of Words Snap:
The Bag of Words Snap is configured to pick up the input data coming in from the Mapper Snap:
The Bag of Words Snap runs the Mapper output (the array of tokenized words in each sentence) against the JSON Parser output (the array listing out the frequency of the 100 most common words in the same dataset) and creates a document detailing the frequency with which the common words appear in each sentence.
Download this pipeline. |
Downloads
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