Marktechpost

OCTOBER 25, 2024

Natural Language Processing (NLP) is a rapidly growing field that deals with the interaction between computers and human language. As NLP continues to advance, there is a growing need for skilled professionals to develop innovative solutions for various applications, such as chatbots, sentiment analysis, and machine translation.

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Analytics Vidhya

JUNE 26, 2023

They allow the network to focus on different aspects of complex input individually until the entire data set is categorized. Introduction Attention models, also known as attention mechanisms, are input processing techniques used in neural networks.

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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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John Snow Labs

AUGUST 9, 2023

The latest version of Legal NLP comes with a new classification model on Law Stack Exchange questions and Named-Entity Recognition on Subpoenas. setOutputCol("class") ) With the model, questions can be categorized. For example, the following text is categorized by the model as belonging to the copyright category.

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IBM Journey to AI blog

FEBRUARY 23, 2024

Beyond the simplistic chat bubble of conversational AI lies a complex blend of technologies, with natural language processing (NLP) taking center stage. NLP translates the user’s words into machine actions, enabling machines to understand and respond to customer inquiries accurately. What makes a good AI conversationalist?

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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. With a strong ability to thoroughly analyze text, these models categorize content into No_Transportation_Insecurity_Or_Unknown and Transportation_Insecurity , providing valuable insights into transportation-related insecurity.

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Marktechpost

MAY 3, 2024

Natural Language Processing (NLP) is integral to artificial intelligence, enabling seamless communication between humans and computers. This interdisciplinary field incorporates linguistics, computer science, and mathematics, facilitating automatic translation, text categorization, and sentiment analysis.

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Unite.AI

JUNE 2, 2023

Based on this, it makes an educated guess about the importance of incoming emails, and categorizes them into specific folders. In addition to the smart categorization of emails, SaneBox also comes with a feature named SaneBlackHole, designed to banish unwanted emails.

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The MLOps Blog

MARCH 23, 2023

NLP models in commercial applications such as text generation systems have experienced great interest among the user. These models have achieved various groundbreaking results in many NLP tasks like question-answering, summarization, language translation, classification, paraphrasing, et cetera.

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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. Regression algorithms —predict output values by identifying linear relationships between real or continuous values (e.g., temperature, salary).

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AssemblyAI

DECEMBER 19, 2024

Natural Language Processing (NLP)  Once speech becomes text, natural language processing, or NLP, models analyze the actual meaning. NLP identifies sentence structure and maps relationships between statements. Advanced ASR models also can provide accurate timing information and confidence scores for each word.

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Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Named Entity Recognition ( NER) Named entity recognition (NER), an NLP technique, identifies and categorizes key information in text.

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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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Marktechpost

APRIL 30, 2024

PEFT’s applicability extends beyond Natural Language Processing (NLP) to computer vision (CV), garnering interest in fine-tuning large-parameter vision models like Vision Transformers (ViT) and diffusion models, as well as interdisciplinary vision-language models.

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Marktechpost

OCTOBER 28, 2023

A model’s capacity to generalize or effectively apply its learned knowledge to new contexts is essential to the ongoing success of Natural Language Processing (NLP). Though it’s generally accepted as an important component, it’s still unclear what exactly qualifies as a good generalization in NLP and how to evaluate it.

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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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Snorkel AI

JULY 2, 2024

Extending weak supervision to non-categorical problems Our research presented in our paper “ Universalizing Weak Supervision ” aimed to extend weak supervision beyond its traditional categorical boundaries to more complex, non-categorical problems where rigid categorization isn’t practical.

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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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Unite.AI

SEPTEMBER 18, 2023

Types of AI in ITSM AI in ITSM can be categorized into three types: automation, chatbots, and predictive analysis. Modern AI chatbots are equipped with Natural Language Processing ( NLP ) to understand and respond to user queries in a more human-like manner. Or consider system outages, the Achilles' heel for any IT-dependent business.

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Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Transformers and Advanced NLP Models : The introduction of transformer architectures revolutionized the NLP landscape.

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Marktechpost

MAY 5, 2024

Consequently, there’s been a notable uptick in research within the natural language processing (NLP) community, specifically targeting interpretability in language models, yielding fresh insights into their internal operations.

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Unite.AI

SEPTEMBER 18, 2023

Blockchain technology can be categorized primarily on the basis of the level of accessibility and control they offer, with Public, Private, and Federated being the three main types of blockchain technologies.

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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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Unite.AI

JUNE 11, 2024

Third, the NLP Preset is capable of combining tabular data with NLP or Natural Language Processing tools including pre-trained deep learning models and specific feature extractors. Finally, the CV Preset works with image data with the help of some basic tools.

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Marktechpost

SEPTEMBER 20, 2024

These methods can be categorized into three main groups: pre-generation tuning, in-generation control, and post-generation parsing. These advancements enhance the reliability and precision of LLM outputs, making Sketch a versatile solution for diverse NLP applications in both research and industrial settings.

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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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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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John Snow Labs

SEPTEMBER 17, 2024

This NLP clinical solution collects data for administrative coding tasks, quality improvement, patient registry functions, and clinical research. Named Entities in Clinical Data Abstraction based on NLP One of the most important tasks in NLP is named-entity recognition. admission discharge, the interaction of drugs, genes, etc.

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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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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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Marktechpost

FEBRUARY 22, 2024

In computer vision, convolutional networks acquire a semantic understanding of images through extensive labeling provided by experts, such as delineating object boundaries in datasets like COCO or categorizing images in ImageNet.

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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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Marktechpost

JUNE 6, 2024

Functions are categorized using ontologies like Gene Ontology (GO) terms, Enzyme Commission (EC) numbers, and Pfam families. It combines non-parametric similarity searches with deep learning inspired by retrieval-augmented techniques in NLP and vision. This method excels, especially for rare and unseen classes.

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Heartbeat

JUNE 22, 2023

Natural language processing (NLP) can help with this. In this post, we’ll look at how natural language processing (NLP) may be utilized to create smart chatbots that can comprehend and reply to natural language requests. What is NLP? Sentiment analysis, language translation, and speech recognition are a few NLP applications.

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Marktechpost

JULY 1, 2024

Natural language processing (NLP) is a branch of artificial intelligence focusing on the interaction between computers and humans using natural language. NLP encompasses various applications, including language translation, sentiment analysis, and conversational agents, significantly enhancing how we interact with technology.

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Marktechpost

APRIL 10, 2024

The abundance of web-scale textual data available has been a major factor in the development of generative language models, such as those pretrained as multi-purpose foundation models and tailored for particular Natural Language Processing (NLP) tasks.

Machine Learning 133

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Snorkel AI

JANUARY 11, 2023

To help brands maximize their reach, they need to constantly and accurately categorize billions of YouTube videos. Using Snorkel Flow, Pixability leveraged foundation models to build small, deployable classification models capable of categorizing videos across more than 600 different classes with 90% accuracy in just a few weeks.

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