Natural Language for query interpretation is fast becoming a requirement for all new search engines. But, creating your own models and NLP pipelines is expensive, complicated, and time consuming. So, why not use those provided in the cloud? We will discuss what NLP intelligence on a query can be gained by using Cloud-Native NLP APIs, and show ways that this understanding can improve the user search experience. We’ll demo this by using Amazon Comprehend and Amazon Comprehend Medical to process queries submitted by users and show how the information returned can be used to customize search results. The methods and information shown here can be used for any Cloud-Native NLP service (e.g. Google NLP AI and Microsoft Luis).
Paul Nelson, Innovation Lead, Accenture
Carlos Maroto, Technical Architect Senior Manager, Accenture Applied Intelligence
Learn how to handle and label insufficient data, compare various text vectorization and modelling techniques, and quickly move from proof of concept to production.
Beginner to Intermediate skilled data scientists with an interest in building text-classification models using natural language processing. Experience coding in Python is a pre-requisite.