Sold by: Mphasis
Deployed on AWS
This solution helps in extracting parent and subsidiary organization relations from text documents.
Overview
Legal entity ownership extraction is an NLP solution that helps identify and classify legal parent and subsidiary organization names in an unstructured text. The solution takes a text file as input. The text can be sourced from documents such as financial statements and legal documents. The solution processes the text to identify all the parent-child organization relationships in the document.
Highlights
- This solution can be leveraged to solve the problem of identifying parent and subsidiary legal entities from noisy text in legal documents. The input can have a maximum of 50000 characters and gives output as a list of dictionaries containing parent and child organization relationships for the given input.
- The solution uses English text as input and uses pretrained language models & name entity recognition techniques to extract organization tags from a given input text. Relationships are then established between the extracted organizations tags using semantics to find the parent and child entities from the given input text. Presently, our solution can identify organization relationships for English text documents only.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
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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 | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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
This is version 3.1
Additional details
Inputs
- Summary
sample_input.txt contains the input data.
- Limitations for input type
- 1) The input has to be a '.txt' file with 'utf-8' encoding. 2) Input file should not contain more than 50000 characters
- Input MIME type
- application/zip, text/plain
https://github.com/Mphasis-ML-Marketplace/Legal-Entity-Ownership-Extraction/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Legal-Entity-Ownership-Extraction/tree/main/input
Resources
Vendor resources
Support
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