Sold by: MphasisÂ
Deployed on AWS
Network measures and visualization of key terms and their associations from a corpus of text
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.
Highlights
- Lightweight NLP solution which provides prominent word associations, visualizations and network properties. We cover the following classes of measures - 1. Degree - presents the most important topic of the central theme discussed in the document; 2. Closeness - signifies the strong association or very closely associated a topic to another topic discussed in the document; 3. Betweeness - shows the critical topic that bridges or associates any two prominent topics of the document; 4. Eigen vector - signifies the highly associated topic with other prominently discussed topics in the document.
- Understanding the prominent topics discussed, identifying and presenting the subject matter content of the document, easy management of document content are the ready extensions of this algorithm. This has varied applications like content generation, content tagging and search engine optimization (SEO).
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $8.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $4.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $8.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input
- The input file should be a text file (.txt) with UTF-8 encoding
- The input file may contain unstructured data in sentences and paragraphs
- The maximum size of the file should be 1MB
- Supported Content type: text/plain
Output
The output from the model is a zip of 3 files, which are:
- 'Image.png' - A png file which is the Word Net associations represented in a fully connected graph.
- '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
- '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.zipSubstitute 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
- Input MIME type
- text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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