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You are tasked with modernizing the contact center for a large technology company to classify incoming calls by product, aiming to expedite routing to the correct support team. The calls have already been transcribed using the Speech-to-Text API. Your primary objectives are to minimize both data preprocessing and development time while ensuring the solution is scalable and cost-effective. Given these constraints, which approach should you take to build the model? Choose the best option.
A
Utilize the Cloud Natural Language API to extract custom entities for classification, considering its ease of integration and predefined models.
B
Develop a custom model from scratch to identify product keywords from the transcribed calls and then apply a classification algorithm, offering maximum customization but requiring significant development effort.
C
Employ AutoML Natural Language to extract custom entities for classification, leveraging its ability to automatically identify and extract custom entities with minimal preprocessing.
D
Use the AI Platform Training built-in algorithms to create a custom model, which may require additional feature engineering but offers flexibility in model selection.
E
Combine the use of AutoML Natural Language for entity extraction with a custom classification algorithm for finer control over the classification process, balancing between development time and customization.