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

Heartbeat

Carefully examining and categorizing these materials can be time-consuming and laborious. On the other hand, NLP-powered algorithms can quickly process and categorize massive amounts of data, minimizing the time necessary for initial case assessment and information retrieval. Natural Language Engineering , 25 (1), 211–217.

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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)?

NLP 52
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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)?

NLP 52
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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

Perform one-hot encoding To unlock the full potential of the data, we use a technique called one-hot encoding to convert categorical columns, like the condition column, into numerical data. One of the challenges of working with categorical data is that it is not as amenable to being used in many machine learning algorithms.

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