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

Heartbeat

Enter Natural Language Processing (NLP) and its transformational power. This is the promise of NLP: to transform the way we approach legal discovery. The seemingly impossible chore of sorting through mountains of legal documents can be accomplished with astonishing efficiency and precision using NLP.

NLP 52
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Future of work: 6 ways AI enhances Online Meetings

Defined.ai blog

This has been a longstanding concern for large companies with distributed workforces, with companies like Apple acquiring startups like Emotient all the way back in 2016. Categorize Me This!”  — Content Categorization: Are you looking for a more organized and efficient way to review and analyze the content from your online meetings?

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Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models

Explosion

Most NLP problems can be reduced to machine learning problems that take one or more texts as input. However, most NLP problems require understanding of longer spans of text, not just individual words. This has always been a huge weakness of NLP models. 2016) presented a model that achieved 86.8% Now we have a solution.

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Can ChatGPT Compete with Domain-Specific Sentiment Analysis Machine Learning Models?

Topbots

SA is a very widespread Natural Language Processing (NLP). Hence, whether general domain ML models can be as capable as domain-specific models is still an open research question in NLP. So, to make a viable comparison, I had to: Categorize the dataset scores into Positive , Neutral , or Negative labels. First, I must be honest.

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Fast & easy baseline text categorization with vw

Hal Daumé III

About a month ago, the paper Bag of Tricks for Efficient Text Categorization was posted to arxiv. See this tutorial for more on how to do NLP in VW.) At the time, I said if they gave me the data, I'd run vw on it and report results. They were nice enough to share the data but I never got around to running it. This took 2.4s

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Text Classification in NLP using Cross Validation and BERT

Mlearning.ai

Introduction In natural language processing, text categorization tasks are common (NLP). We use categorical crossentropy for loss along with sigmoid as an activation function for our model Figure 14 Figure 15 shows how we tracked convergence for the neural network. Uysal and Gunal, 2014). Manning C. and Schutze H., Malik, A.

BERT 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

Use natural language processing (NLP) in Amazon HealthLake to extract non-sensitive data from unstructured blobs. We can see that Amazon HeathLake NLP interprets this as containing the condition “stroke” by querying for the condition record that has the same patient ID and displays “stroke.” mg/actuat / salmeterol 0.05

ML 98