Remove 2016 Remove Categorization Remove Natural Language Processing
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Commonsense Reasoning for Natural Language Processing

Probably Approximately a Scientific Blog

The release of Google Translate’s neural models in 2016 reported large performance improvements: “60% reduction in translation errors on several popular language pairs”. Figure 1: adversarial examples in computer vision (left) and natural language processing tasks (right).

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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). So, to make a viable comparison, I had to: Categorize the dataset scores into Positive , Neutral , or Negative labels. Interestingly, ChatGPT tended to categorize most of these neutral sentences as positive. finance, entertainment, psychology).

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

Explosion

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. A four-step strategy for deep learning with text Embedded word representations, also known as “word vectors”, are now one of the most widely used natural language processing technologies.

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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? However, in this sea of complexity, NLP offers a ray of hope.

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Foundation models: a guide

Snorkel AI

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Devlin et al.

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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. 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.

ML 98
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Advancing Human-AI Interaction: Exploring Visual Question Answering (VQA) Datasets

Heartbeat

Visual Question Answering (VQA) stands at the intersection of computer vision and natural language processing, posing a unique and complex challenge for artificial intelligence. is a significant benchmark dataset in computer vision and natural language processing. or Visual Question Answering version 2.0,