Becoming Human

MAY 9, 2024

Artificial intelligence, machine learning, natural language processing, and other related technologies are paving the way for a smarter “everything.” Where interpreting raw financial data has become easier NLP, it is also helping us make better predictions and financial decisions. How Does Data Labeling Work in Finance?

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Lexalytics

MAY 25, 2021

One of the most important and most-used functions in text analytics and NLP is sentiment analysis — the process of determining whether a word, phrase, or document is positive, negative, or neutral. At Lexalytics, an InMoment company, our approach has been to hand-categorize content into polar and non-polar groups.

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IBM Journey to AI blog

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Why are Scope 3 emissions difficult to calculate?

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NVIDIA Developer

JUNE 2, 2021

Applications for natural language processing (NLP) have exploded in the past decade. Modern techniques can capture the nuance, context, and sophistication of language, just as humans do. Multiple-choice questions evaluate students’ understanding of the NLP concepts presented in the class.

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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. Recent approaches automate circuit discovery, enhancing interpretability.

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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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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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Heartbeat

AUGUST 23, 2023

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. This is the promise of NLP: to transform the way we approach legal discovery. But exactly what is NLP , and how can it facilitate legal discovery?

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Topbots

APRIL 11, 2023

In the rapidly evolving field of artificial intelligence, natural language processing has become a focal point for researchers and developers alike. 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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Probably Approximately a Scientific Blog

JANUARY 12, 2021

In NLP, dialogue systems generate highly generic responses such as “I don’t know” even for simple questions. Figure 1: adversarial examples in computer vision (left) and natural language processing tasks (right). Is commonsense knowledge already captured by pre-trained language models?

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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. This approach has demonstrated effectiveness in natural language processing and reinforcement learning.

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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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John Snow Labs

OCTOBER 19, 2023

Understanding the Impact of Bias on NLP Models Why test NLP models for Bias? Natural Language Processing (NLP) models rely heavily on bias to function effectively. This is due to the fact that bias helps NLP models to identify important features and relationships among data points.

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AssemblyAI

MAY 14, 2024

It uses natural language processing to identify and organize discussion points, decisions, and future tasks. It automatically categorizes, summarizes, and extracts actionable insights from customer calls, such as flagging questions and complaints.   Fireflies.ai

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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 NLP understands and predicts law, converts unstructured text into a meaningful format that computers can understand and analyze. billion by 2026.

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Heartbeat

JULY 11, 2023

Photo by Oleg Laptev on Unsplash By improving many areas of content generation, optimization, and analysis, natural language processing (NLP) plays a crucial role in content marketing. It entails employing algorithms and techniques to process and extract meaning from human language. articles, videos).

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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.

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Marktechpost

MARCH 10, 2024

Developing and refining Large Language Models (LLMs) has become a focal point of cutting-edge research in the rapidly evolving field of artificial intelligence, particularly in natural language processing. A significant innovation in this domain is creating a specialized tool to refine the dataset compilation process.

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Marktechpost

DECEMBER 18, 2023

The attention mechanism has played a significant role in natural language processing and large language models. The researchers emphasized that transformers utilize an old-school method similar to support vector machines (SVM) to categorize data into relevant and non-relevant information. Check out the Paper.

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ODSC - Open Data Science

MAY 24, 2023

Generative NLP Models in Customer Service: Evaluating Them, Challenges, and Lessons Learned in Banking Editor’s note: The authors are speakers for ODSC Europe this June. Be sure to check out their talk, “ Generative NLP models in customer service. How to evaluate them?

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Becoming Human

MAY 9, 2024

In this article, we’ll talk about what named entity recognition is and why it holds such an integral position in the world of natural language processing. Introduction about NER Named entity recognition (NER) is a fundamental aspect of natural language processing (NLP).

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Artificial Corner

JULY 30, 2023

Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. Image by YouTube video “Introduction to large language models” on YouTube Channel “Google Cloud Tech” What are Large Language Models? Natural Language Processing (NLP) is a subfield of artificial intelligence.

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Marktechpost

DECEMBER 25, 2023

This tool enhances data exploration by integrating cutting-edge Natural Language Processing (NLP) techniques. Imagine organizing and categorizing information in a way that reveals valuable insights without manually sifting through endless data points.

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Unite.AI

APRIL 16, 2024

One notable innovation in regulatory monitoring is the integration of natural language processing (NLP) and machine learning algorithms. By identifying relevant updates, extracting key information, and categorizing regulatory changes based on their potential impact, these technologies streamline the monitoring process.

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Marktechpost

JULY 17, 2023

Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques are used in the process of AI content detection to automatically recognize and assess the content of a text. AI is a tool that detects plagiarism in written content using natural language processing. Originality.AI

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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.

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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). FastEmbed is a compact yet powerful library for generating embeddings in large databases.

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Marktechpost

JULY 4, 2023

Natural language processing (NLP) activities, including speech-to-text, sentiment analysis, text summarization, spell-checking, token categorization, etc., rely on Language Models as their foundation. Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model.

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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. AI-driven chatbots are here to help.

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Marktechpost

JANUARY 21, 2024

Large Language Models (LLMs) have exhibited remarkable prowess across various natural language processing tasks. However, applying them to Information Retrieval (IR) tasks remains a challenge due to the scarcity of IR-specific concepts in natural language.

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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.

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Artificial Corner

JUNE 18, 2023

ChatGPT released by OpenAI is a versatile Natural Language Processing (NLP) system that comprehends the conversation context to provide relevant responses. Although little is known about construction of this model, it has become popular due to its quality in solving natural language tasks.

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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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AssemblyAI

JANUARY 23, 2024

  By leveraging Deep Learning architectures and training on vast amounts of data, LLMs can process and understand more nuance and context in human language than traditional Natural Language Processing (NLP) models. 

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Mlearning.ai

MARCH 4, 2023

Named Entity Recognition (NER) is a natural language processing technique that involves identifying and categorizing named entities (such… Continue reading on MLearning.ai »

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John Snow Labs

APRIL 22, 2024

While large language models (LLMs) have demonstrated impressive capabilities in various natural language processing (NLP) tasks, their performance in this domain has been limited by the inherent complexities of medical language and the nuances involved in interpreting clinical narratives.

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Aiiot Talk

JULY 18, 2023

Consider the applications of natural language processing (NLP) in an LMS. NLP often accompanies it to translate language into a medium it can understand. With AI, the entire process becomes much faster. Since it operates on specific data sets, you can train it to give them relevant support.

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