Which solution can the team build MOST quickly to meet these requirements?
Use Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.
Use an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.
Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
Explanations:
While Amazon Comprehend can handle part-of-speech tagging and key phrase extraction, it does not support custom classification algorithms directly. The team would need to find another solution for the classification task, making this option less efficient for their needs.
This option involves using multiple services, which could complicate the integration process. While it utilizes an NLP library for part-of-speech tagging and Amazon Comprehend for key phrase extraction, it may take longer to set up and integrate with AWS Deep Learning Containers for the custom classifier.
Amazon Comprehend is suitable for part-of-speech tagging and key phrase extraction, but it does not support building custom classifiers using the LDA algorithm in a straightforward manner. This option fails to meet the requirement for a custom classification algorithm built with Apache MXNet.
This option effectively utilizes Amazon Comprehend for both part-of-speech tagging and key phrase extraction, which are handled quickly and efficiently. It also allows the team to leverage AWS Deep Learning Containers to build and deploy their custom classifier using Apache MXNet, aligning perfectly with their existing work.