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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

HyperGraf Word Associations Network

By: Mphasis Latest Version: 3.2

Network measures and visualization of key terms and their associations from a corpus of text

Product Overview

Word Associations Network depicts the most prominent topics and their inter-linkages from a corpus of text. Each topic observed as Nouns and Verbs is represented by frequently associated contextual information in a word network, along with centrality measures of associations like betweeness, closeness, eigen vector and weighted degree.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

1) The input file should be a text file (.txt) with UTF-8 encoding
2) The input file may contain unstructured data in sentences and paragraphs 3) The maximum size of the file should be 1MB 4) Supported Content type: text/plain

Output

The output from the model is a zip of 3 files, which are: 1) 'Image.png' - A png file which is the Word Net associations represented in a fully connected graph. 2) 'Centrality_measures.csv ' - A csv file which has the centrality measures.

  • Degree - presents the most important topic of the central theme discussed in the document
  • Betweeneness - shows the critical topic that bridges or associates any two prominent topics of the document
  • Closeness - signifies the strong association or very closely associated a topic to another topic discussed in the document
  • Eigen Vector - signifies the highly associated topic with other prominently discussed topics in the document

3) 'My_edgelist.csv' - A csv file which has the edge list of weights.

  • Weights - signifies the frequency of association between the nodes

Supported Content type: application/zip

Invoking endpoint

AWS CLI Command

If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:

aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.txt --content-type text/plain --accept application/zip output.zip

Substitute the following parameters:

  • endpoint-name - name of the inference endpoint where the model is deployed
  • input.txt - input file
  • text/plain - MIME type of the given input file (above)
  • output.zip - filename where the inference results are written to.

Resources

Sample Notebook Sample Input Sample Output

Additional Resources

End User License Agreement

By subscribing to this product you agree to terms and conditions outlined in the product End User License Agreement (EULA)

Support Information

HyperGraf Word Associations Network

For any assistance, please reach out at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

AWS Infrastructure

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Refund Policy

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