Analytics Vidhya

AUGUST 17, 2022

A team at Google Brain developed Transformers in 2017, and they are now replacing RNN models like long short-term memory(LSTM) as the model of choice for NLP […]. Introduction Transformers were one of the game-changer advancements in Natural language processing in the last decade.

Data Science 248

Data Science Natural Language Processing NLP Deep Learning 248

Towards AI

JULY 21, 2023

Photo by Will Truettner on Unsplash NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 07.26.20 The last known comms from 3301 came in April 2017 via Pastebin post. These 2 repos encompass NLP and Speech modeling. Primus The Liber Primus is unsolved to this day.

NLP 83

NLP Inference Engine Natural Language Processing Chatbots 83

Towards AI

JULY 20, 2023

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. That’s great news for researchers who often work on SLRs because the traditional process is mind-numbingly slow: An analysis from 2017 found that SLRs take, on average, 67 weeks to produce.

Data Extraction 82

Data Extraction NLP Natural Language Processing Automation 82

Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

databricks

APRIL 19, 2023

Introduction Back in 2017, a group of researchers at Google AI published a paper that introduced a transformer model architecture that changed all.

NLP 80

NLP AI AI 80

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

NLP 98

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Towards AI

FEBRUARY 20, 2024

Exploring the encoder-decoder magic in NLP behind LLMsImage created by the author. The state-of-the-art Natural Language Processing (NLP) models used to be Recurrent Neural Networks (RNN) among others. And then came Transformers. Transformer architecture significantly improved natural language task performance compared to earlier RNNs.

Neural Network 80

Neural Network Natural Language Processing NLP AI 80

Towards AI

FEBRUARY 6, 2024

Photo by Johannes Plenio on Unsplash Since the 2017 paper “Attention Is All You Need” invented the Transformer architecture, natural language processing (NLP) has seen tremendous growth. Leon Eversberg Originally published on Towards AI. LLMs are evolving at a rapid speed.

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LLM Natural Language Processing Large Language Models NLP 93

Towards AI

JUNE 6, 2023

unsplash Attention-based transformers have revolutionized the AI industry since 2017. Now it’s possible to have deep learning models with no limitation for the input size.

Neural Network 92

Neural Network Computer Vision Deep Learning NLP 92

Topbots

JUNE 22, 2023

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. It has several applications and thus can be used in several domains (e.g., finance, entertainment, psychology).

Machine Learning 111

Machine Learning ChatGPT Natural Language Processing Categorization 111

Towards AI

APRIL 19, 2024

PwC 👉Industry domain: AI, Professional services, Business intelligence, Consulting, Cybersecurity, Generative AI 👉Location: 73 offices 👉Year founded: 1998 👉Programming Languages Deployed: Java, Google Cloud, Microsoft SQL, jQuery, Pandas, R, Oracle 👉Benefits: Hybrid workspace, Child care and parental leave, flexible (..)

Generative AI 87

Generative AI AI AI Artificial Intelligence 87

Towards AI

JUNE 4, 2023

BERT Transformer Source: Image created by the author + Stable Diffusion (All Rights Reserved) In the context of machine learning and NLP, a transformer is a deep learning model introduced in a paper titled “Attention is All You Need” by Vaswani et al. The model was proposed as a way to improve the performance of translation systems.

BERT 52

BERT Deep Learning Machine Learning NLP 52

Towards AI

APRIL 8, 2024

While numerous techniques have been explored, methods harnessing natural language processing (NLP) have demonstrated strong performance. Word2Vec, a widely-adopted NLP algorithm has proven to be an efficient and valuable tool that is now applied across multiple domains, including recommendation systems.

Natural Language Processing 68

Natural Language Processing NLP Neural Network Algorithm 68

AWS Machine Learning Blog

FEBRUARY 6, 2024

Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. This allows the model to learn long-range dependencies between words, which is essential for many NLP tasks, such as machine translation and text summarization.

Large Language Models 97

Large Language Models BERT NLP Data Scientist 97

ODSC - Open Data Science

JUNE 9, 2023

The last 12 years though, is where some of the big magic has happened in NLP. Word vectorization is an NLP methodology used to map words or phrases from a vocabulary to a corresponding vector of real numbers used to find word predictions and word similarities or semantics. GPT models are designed to generate text.

Natural Language Processing 52

Natural Language Processing ChatGPT Generative AI BERT 52

Mlearning.ai

APRIL 21, 2023

Natural Language Processing Transformers, the neural network architecture, that has taken the world of natural language processing (NLP) by storm, is a class of models that can be used for both language and image processing. One of the earliest representation models used in NLP was the Bag of Words (BoW) model.

Neural Network 52

Neural Network Natural Language Processing ChatGPT NLP 52

Mlearning.ai

JULY 2, 2023

In natural language processing (NLP), the “Attention is All You Need” paper introduced a groundbreaking neural network architecture called the Transformer. Its ability to capture long-range dependencies efficiently has made it a foundational model in various NLP applications.

Neural Network 52

Neural Network NLP Natural Language Processing Large Language Models 52

Towards AI

JULY 21, 2023

The Ninth Wave (1850) Ivan Aivazovsky NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 09.13.20 declassified Blast from the past: Check out this old (2017) blog post from Google introducing transformer models. Aere Perrenius Welcome back. Hope you enjoyed your week! Data conversion and encoding.

NLP 88

NLP Natural Language Processing Machine Learning ML 88

Salmon Run

NOVEMBER 30, 2020

The BERT (Bidirectional Encoder Representation from Transformers) model was proposed in the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin, et al, 2019). BERT is the encoder part of an encoder-decoder architecture called Transformers, that was proposed in Attention is all you need (Vaswani, et al.,

BERT 40

BERT NLP Python 40

John Snow Labs

JUNE 7, 2023

The Normalizer annotator in Spark NLP performs text normalization on data. The Normalizer annotator in Spark NLP is often used as part of a preprocessing step in NLP pipelines to improve the accuracy and quality of downstream analyses and models. These transformations can be configured by the user to meet their specific needs.

NLP 52

NLP Natural Language Processing Metadata Python 52

John Snow Labs

MAY 25, 2023

Rule-based sentiment analysis in Natural Language Processing (NLP) is a method of sentiment analysis that uses a set of manually-defined rules to identify and extract subjective information from text data. Using Spark NLP, it is possible to analyze the sentiment in a text with high accuracy.

NLP 52

NLP Machine Learning Neural Network ML 52

John Snow Labs

JUNE 14, 2023

Stopwords removal in natural language processing (NLP) is the process of eliminating words that occur frequently in a language but carry little or no meaning. Stopwords cleaning in Spark NLP is the process of removing stopwords from the text data. Stopwords are commonly occurring words (like the, a, and, in , etc.)

NLP 52

NLP Natural Language Processing Metadata Python 52

John Snow Labs

MAY 26, 2023

Sentence embeddings with Transformers are a powerful natural language processing (NLP) technique that use deep learning models known as Transformers to encode sentences into fixed-length vectors that can be used for a variety of NLP tasks. Introduction to Spark NLP Spark NLP is an open-source library maintained by John Snow Labs.

NLP 52

NLP BERT Natural Language Processing Deep Learning 52

NLP People

JANUARY 2, 2019

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). BERT is a new milestone in NLP. 7-Have we Finally Solved Machine Translation?

BERT 52

BERT Natural Language Processing Computational Linguistics NLP 52

Sebastian Ruder

JUNE 6, 2022

Seeing the emergence of such multilingual multimodal approaches is particularly encouraging as it is an improvement over the previous year’s ACL where multimodal approaches mainly dealt with English (based on an analysis of “multi-dimensional” NLP research we did for an ACL 2022 Findings paper ). Hershcovich et al.

NLP 52

NLP Natural Language Processing Computational Linguistics Neural Network 52

John Snow Labs

MAY 18, 2023

Word embeddings are considered as a type of representation used in natural language processing (NLP) to capture the meaning of words in a numerical form. Word embeddings are used in natural language processing (NLP) as a technique to represent words in a numerical format.

NLP 52

NLP Machine Learning Python Natural Language Processing 52

Mlearning.ai

JUNE 7, 2023

We have the IPL data from 2008 to 2017. Also do check out my other Machine Learning projects , Deep Learning projects , Computer Vision projects , Flask projects , NLP projects. We will also be building a beautiful-looking interactive Flask model. Working Video of our App [link] 7.

Machine Learning 52

Machine Learning Python Computer Vision Deep Learning 52

Towards AI

JULY 19, 2023

The Ninth Wave (1850) Ivan Aivazovsky NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 09.13.20 link] Blast from the past: Check out this old (2017) blog post from Google introducing transformer models. Wild to see how much progress that’s been made in the field of NLP in the last couple of years. ?

NLP 40

NLP Natural Language Processing Machine Learning ML 40

John Snow Labs

JUNE 5, 2023

Sentence detection in Spark NLP is the process of identifying and segmenting a piece of text into individual sentences using the Spark NLP library. Sentence Detection in Spark NLP is the process of automatically identifying the boundaries of sentences in a given text.

NLP 52

NLP Natural Language Processing Deep Learning Algorithm 52

Sebastian Ruder

AUGUST 18, 2019

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks. However, transfer learning is not a recent phenomenon in NLP.

NLP 75

NLP BERT Natural Language Processing Computational Linguistics 75

Sebastian Ruder

JULY 31, 2020

Natural language processing (NLP) research predominantly focuses on developing methods that work well for English despite the many positive benefits of working on other languages. Most of the world's languages are spoken in Asia, Africa, the Pacific region and the Americas. Each feature has 5.93 categories on average.

NLP 78

NLP Natural Language Processing Machine Learning ML 78

Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). This is less of a problem in NLP where unsupervised pre-training involves classification over thousands of word types.

NLP 52

NLP ML Computer Vision Natural Language Processing 52

Unite.AI

NOVEMBER 16, 2023

My second company was an alternative asset management company I co-founded in 2017 prior to the ICO-wave in crypto. This was my first exposure to NLP where we used sentiment analysis of social media data as an investment strategy. At the same time, I also learned a lot about myself and about what I wanted to work in.

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LLM NLP Artificial Intelligence Artificial Intelligence 147

Viso.ai

JANUARY 16, 2024

’ It uses natural language processing (NLP) for the descriptions, allowing the ANN to develop a semantic understanding. Over the 5 years between 2017-2021, AI patent grants grew on average by 18.2% Emotional Perception AI Ltd argues that this is going a step beyond conventional categorization.

Neural Network 52

Neural Network Computer Vision Categorization Algorithm 52

Explosion

AUGUST 1, 2019

Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. It features consistent and easy-to-use interfaces to several models, which can extract features to power your NLP pipelines. apple2 = nlp("Apple sold fewer iPhones this quarter.") print(apple1[0].similarity(apple2[0]))

BERT 52

BERT NLP Neural Network Categorization 52

Sebastian Ruder

JANUARY 6, 2020

This post gathers ten ML and NLP research directions that I found exciting and impactful in 2019.  Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., 2019) find winning ticket initialisations also for LSTMs and Transformers in NLP and RL models. 2019 ) and other variants.

NLP 52

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NLP People

JULY 31, 2017

According to Bloomberg , scientists even met in 2017 to discuss the apparently very real dangers behind AI. Elon Musk and Stephen Hawking have voiced their concerns about self-aware AI, and UK parliament has recently proposed an ethics committee dealing with machines. Is there any danger?

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Artificial Intelligence Artificial Intelligence Robotics AI 52