Amazon Comprehend is a natural-language processing (NLP) service offering pre-trained and custom APIs for deriving insights from textual data. It allows training custom named entity recognition (NER) models to extract business-specific entities from documents. The pre-labeling tool automates document annotation using existing tabular entity data, reducing manual effort. The tool accelerates custom entity recognition model training in Amazon Comprehend, making it more accessible.
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Amazon Comprehend: Practical AI Solutions for Middle Managers
Solution Overview
Amazon Comprehend is an NLP service that offers pre-trained and custom APIs to extract insights from textual data. With custom named entity recognition (NER) models, businesses can extract specific entities like location, person names, and dates unique to their operations.
Value Proposition
By leveraging Amazon Comprehend, companies can streamline the process of training accurate custom entity recognition models, reducing manual effort and enhancing data insights.
Practical Solutions
To simplify the preparation of training data, a pre-labeling tool has been developed using AWS Step Functions. This tool automatically pre-annotates documents using existing tabular entity data, significantly reducing the manual work required to train custom entity recognition models in Amazon Comprehend.
Architecture
The pre-labeling tool consists of multiple AWS Lambda functions orchestrated by a Step Functions state machine. It utilizes two techniques to generate pre-annotations: fuzzy matching and a pre-trained Amazon Comprehend entity recognizer model.
Deployment
Managers can easily deploy the pre-labeling tool by cloning the repository to their local machine and leveraging the AWS Serverless Application Model (AWS SAM) for infrastructure setup.
Practical Implementation
Before using the pre-labeling tool, managers can prepare their data by creating a pre-manifest file that maps PDF documents with the entities to be extracted. This file contains the expected text to extract and the corresponding entity type.
Running the Tool
Once the pre-manifest file is prepared, managers can execute the pre-labeling tool, providing necessary inputs such as the premanifest, prefix, entity types, and other optional parameters. The tool then automates the annotation process and generates outputs for further use.
Conclusion
The pre-labeling tool offers a powerful way for companies to leverage existing tabular data, accelerating the process of training custom entity recognition models in Amazon Comprehend. It enables quick unlocking of the value of historical entity data, making custom entity recognition with Amazon Comprehend more accessible than ever.
About the Authors: Oskar Schnaack and Romain Besombes are experts in the field of AI and machine learning, passionate about making these technologies accessible and impactful for customers.
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