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NLP in Legal Discovery: Unleashing Language Processing for Faster Case Analysis

Heartbeat

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. But what if there was a way to unravel this language puzzle swiftly and accurately?

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Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

AWS Machine Learning Blog

The high-level steps involved in the solution are as follows: Use AWS Step Functions to orchestrate the health data anonymization pipeline. Use Amazon Athena queries for the following: Extract non-sensitive structured data from Amazon HealthLake. Perform one-hot encoding with Amazon SageMaker Data Wrangler.

ML 101
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Find Your AI Solutions at the ODSC West AI Expo

ODSC - Open Data Science

Ikigai Labs Ikigai Labs is a company that provides a platform for building and managing natural language processing models. Outerbounds Founded in 2016, Outerbounds is a company that provides a platform for building and managing anomaly detection models.

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The Pros and Cons of Using the Top 5 Open-Source Named Entity Recognition Datasets

Defined.ai blog

Named Entity Recognition (NER) is a natural language processing (NLP) subtask that involves automatically identifying and categorizing named entities mentioned in a text, such as people, organizations, locations, dates, and other proper nouns. What is Named Entity Recognition (NER)?

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The Pros and Cons of Using the Top 5 Open-Source Named Entity Recognition Datasets

Defined.ai blog

Named Entity Recognition (NER) is a natural language processing (NLP) subtask that involves automatically identifying and categorizing named entities mentioned in a text, such as people, organizations, locations, dates, and other proper nouns. What is Named Entity Recognition (NER)?