Flipboard

FEBRUARY 16, 2023

Natural language processing ( NLP ), while hardly a new discipline, has catapulted into the public consciousness these past few months thanks in large part to the generative AI hype train that is ChatGPT. ‘Data-centric’ NLP With NLP one of the hot AI trends of the moment, Kern AI today announced that it has raised €2.7

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AWS Machine Learning Blog

OCTOBER 24, 2023

In today’s information age, the vast volumes of data housed in countless documents present both a challenge and an opportunity for businesses. Traditional document processing methods often fall short in efficiency and accuracy, leaving room for innovation, cost-efficiency, and optimizations. However, the potential doesn’t end there.

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IDP LLM Prompt Engineering Prompt Engineer 133

AWS Machine Learning Blog

DECEMBER 23, 2024

Also, end-user queries are not always aligned semantically to useful information in provided documents, leading to vector search excluding key data points needed to build an accurate answer. Translating natural language into vectors reduces the richness of the information, potentially leading to less accurate answers.

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Generative AI Natural Language Processing Prompt Engineering Prompt Engineer 123

John Snow Labs

AUGUST 21, 2023

We are delighted to announce a suite of remarkable enhancements and updates in our latest release of Healthcare NLP. setInputCols(["document"]).setOutputCol("sql") Text2SQL , module to translate text prompts into accurate SQL queries We are excited to introduce our latest innovation, the Text2SQL annotator.

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NLP Categorization Natural Language Processing Automation 98

John Snow Labs

SEPTEMBER 17, 2024

This NLP clinical solution collects data for administrative coding tasks, quality improvement, patient registry functions, and clinical research. Second, the information is frequently derived from natural language documents or a combination of structured, imaging, and document sources.

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NLP Natural Language Processing Categorization Automation 52

Marktechpost

MAY 9, 2024

In Natural Language Processing (NLP) tasks, data cleaning is an essential step before tokenization, particularly when working with text data that contains unusual word separations such as underscores, slashes, or other symbols in place of spaces. The post Is There a Library for Cleaning Data before Tokenization?

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John Snow Labs

NOVEMBER 5, 2024

This blog post explores how John Snow Labs’ Healthcare NLP & LLM library is transforming clinical trials by using advanced NER models to efficiently filter through large datasets of patient records. link] John Snow Labs’ Healthcare NLP & LLM library offers a powerful solution to streamline this process.

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AWS Machine Learning Blog

APRIL 19, 2023

The ability to effectively handle and process enormous amounts of documents has become essential for enterprises in the modern world. Due to the continuous influx of information that all enterprises deal with, manually classifying documents is no longer a viable option.

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Categorization Machine Learning Natural Language Processing ML 92

IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. K-means clustering is commonly used for market segmentation, document clustering, image segmentation and image compression.

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ODSC - Open Data Science

OCTOBER 27, 2023

Companies in sectors like healthcare, finance, legal, retail, and manufacturing frequently handle large numbers of documents as part of their day-to-day operations. These documents often contain vital information that drives timely decision-making, essential for ensuring top-tier customer satisfaction, and reduced customer churn.

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IDP LLM Large Language Models Data Science 98

John Snow Labs

APRIL 30, 2024

This article will delve into the significance of NER (Named Entity Recognition) detection in OCR (Optical Character Recognition) and showcase its application through the John Snow Labs NLP library with visual features installed. How does Visual NLP come into action? What are the components that will be used for NER in Visual NLP?

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NLP Categorization Natural Language Processing Explainability 52

IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

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Data Mining Convolutional Neural Networks Categorization Machine Learning 170

Topbots

APRIL 11, 2023

BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers. This model marked a new era in NLP with pre-training of language models becoming a new standard. What is the goal? accuracy on SQuAD 1.1

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NLP BERT Large Language Models Natural Language Processing 98

John Snow Labs

JANUARY 30, 2025

This blog post explores how John Snow Labs Healthcare NLP & LLM library revolutionizes oncology case analysis by extracting actionable insights from clinical text. Together, these use cases illustrate the transformative potential of combining Healthcare NLP and LLMs for oncology case analysis.

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NLP Large Language Models LLM Data Analysis 52

AssemblyAI

JANUARY 23, 2025

Broadly, Python speech recognition and Speech-to-Text solutions can be categorized into two main types: open-source libraries and cloud-based services. Sphinx is relatively lightweight compared to other speech-to-text solutions, supports multiple languages, and offers extensive developer documentation and FAQs.

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Python Convolutional Neural Networks Neural Network OpenAI 130

IBM Journey to AI blog

AUGUST 1, 2023

Foundation models can be trained to perform tasks such as data classification, the identification of objects within images (computer vision) and natural language processing (NLP) (understanding and generating text) with a high degree of accuracy. An open-source model, Google created BERT in 2018. All watsonx.ai

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Marktechpost

JANUARY 21, 2024

This dataset focuses on three pivotal aspects prevalent in search-related tasks: query understanding, document understanding, and the intricate relationship between queries and documents. In the context of search tasks, distinct from typical NLP tasks, the focus revolves around queries and documents.

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Large Language Models Natural Language Processing Categorization NLP 140

Marktechpost

JULY 6, 2024

Natural language processing (NLP) in artificial intelligence focuses on enabling machines to understand and generate human language. One of the major challenges in NLP is effectively evaluating the performance of LLMs on tasks that require processing long contexts. The methodology involves several detailed steps.

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AI Researcher AI Research Large Language Models Natural Language Processing 112

Unite.AI

APRIL 16, 2024

AI-powered tools offer the capability to navigate vast databases, analyze clinical research data, streamline document searches, and access worldwide regulatory news. One notable innovation in regulatory monitoring is the integration of natural language processing (NLP) and machine learning algorithms.

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AWS Machine Learning Blog

JUNE 18, 2024

Many companies across all industries still rely on laborious, error-prone, manual procedures to handle documents, especially those that are sent to them by email. Intelligent automation presents a chance to revolutionize document workflows across sectors through digitization and process optimization.

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

Amazon Comprehend is a natural-language processing (NLP) service that uses machine learning to uncover valuable insights and connections in text. Knowledge management – Categorizing documents in a systematic way helps to organize an organization’s knowledge base. politics, sports) that a document belongs to.

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Categorization Machine Learning Data Scientist Natural Language Processing 116

Becoming Human

MAY 9, 2024

Where interpreting raw financial data has become easier NLP, it is also helping us make better predictions and financial decisions. NLP in finance includes semantic analysis, information extraction, and text analysis. Within NLP, data labeling allows machine learning models to isolate finance-related variables in different datasets.

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Unite.AI

JANUARY 11, 2024

Text embeddings are vector representations of words, sentences, paragraphs or documents that capture their semantic meaning. They serve as a core building block in many natural language processing (NLP) applications today, including information retrieval, question answering, semantic search and more.

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Large Language Models Prompt Engineer Prompt Engineering BERT 147

Unite.AI

AUGUST 27, 2024

Using Natural Language Processing (NLP) and the latest AI models, Perplexity AI moves beyond keyword matching to understand the meaning behind questions. Here's what Perplexity can do: Summarize content: Condense lengthy articles, documents, and webpages into concise summaries to grasp key points quickly.

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ChatGPT AI AI AI Tools 264

ODSC - Open Data Science

SEPTEMBER 12, 2024

Now that artificial intelligence has become more widely accepted, some daring companies are looking at natural language processing (NLP) technology as the solution. Naturally, its high penetration rate has given way to exploration into machine learning subsets like deep learning and NLP. What Is Compliance Monitoring in Banking?

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NLP Automation Machine Learning Data Science 52

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. identifying the “emotional tone” of a particular document). identifying the “emotional tone” of a particular document).

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Deep Learning Convolutional Neural Networks NLP Neural Network 98

AWS Machine Learning Blog

JUNE 25, 2024

Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. By using the pre-trained knowledge of LLMs, zero-shot and few-shot approaches enable models to perform NLP with minimal or no labeled data.

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Automation Prompt Engineering Prompt Engineer Categorization 120

Marktechpost

OCTOBER 22, 2023

Words and phrases can be effectively represented as vectors in a high-dimensional space using embeddings, making them a crucial tool in the field of natural language processing (NLP). For very large documents or vocabulary sizes, this matrix can become unmanageably enormous.

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Python Natural Language Processing Categorization NLP 112

Heartbeat

AUGUST 23, 2023

image source: stoodnt Time is important in the broad domain of legal discovery, where mountains of documents hide the answers to difficult cases. Every minute spent digesting jargon-filled texts and searching through mountains of legal documents delays justice and incurs significant costs.

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NLP Natural Language Processing Algorithm Categorization 52

John Snow Labs

JUNE 26, 2024

This blog post explores how John Snow Labs’ Healthcare NLP models are revolutionizing the extraction of critical insights on opioid use disorder. Here, NLP offers a powerful solution. Let us start with a short Spark NLP introduction and then discuss the details of opioid drugs analysis with some solid results.

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NLP Categorization Large Language Models Data Analysis 52

AWS Machine Learning Blog

SEPTEMBER 13, 2024

Using natural language processing (NLP) and OpenAPI specs, Amazon Bedrock Agents dynamically manages API sequences, minimizing dependency management complexities. The policy agent accesses the Policy Information API to extract answers to insurance-related questions from unstructured policy documents such as PDF files.

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Metadata Automation LLM NLP 128

John Snow Labs

MAY 19, 2023

Natural Language Processing ( NLP ) is changing the way the legal sector operates. According to a report, the NLP market size is expected to reach $27.6 Organizing documents, and looking for specific information in huge amounts of documents can be frustrating for lawyers and their clients. billion by 2026.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI , allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. This process involves updating the model’s weights to improve its performance on targeted applications.

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LLM Prompt Engineering Prompt Engineer Generative AI 92

John Snow Labs

MARCH 27, 2025

Introduction to Pretrained Zero-Shot NER for Healthcare NLP Named Entity Recognition (NER) is a cornerstone of NLP tasks, yet traditional NER approaches often require extensive domain-specific training to achieve high performance. Step 1: Set Up the Environment First, we need to set up the Spark NLP Healthcare library.

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NLP Categorization Python Large Language Models 52

Marktechpost

AUGUST 15, 2023

Large language models (LLMs) have achieved amazing results in a variety of Natural Language Processing (NLP), Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks in recent years. The team has performed a thorough study and categorization of numerous contemporary research projects that make use of these tactics.

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Large Language Models Categorization Natural Language Processing LLM 98

John Snow Labs

JANUARY 30, 2025

This blog explores the performance and comparison of de-identification services provided by Healthcare NLP, Amazon, and Azure, focusing on their accuracy when applied to a dataset annotated by healthcare experts. Dataset For this benchmark, we utilized 48 open-source documents annotated by domain experts from John Snow Labs.

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Marktechpost

JULY 7, 2024

Significant progress has been made in natural language processing (NLP) and information retrieval (IR) technologies. This involves using high-quality, ranked documents as training data to help LLMs understand queries better and generate more accurate responses.

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