Marek Rei

JANUARY 2, 2018

It has been a very productive year for NLP and ML research. Venues First, let’s look at different publication venues between 2012-2017. Most other venues are also growing rapidly, with 2017 being the biggest year ever for ICML, ICLR, EMNLP, EACL and CoNLL. Smith (Washington).

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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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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.

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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.

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Unite.AI

JULY 24, 2024

Over the past decade, advancements in machine learning, Natural Language Processing (NLP), and neural networks have transformed the field. In 2017, Apple introduced Core ML , a machine learning framework that allowed developers to integrate AI capabilities into their apps. Notable acquisitions include companies like Xnor.a

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Marktechpost

APRIL 6, 2024

Transformers have transformed the field of NLP over the last few years, with LLMs like OpenAI’s GPT series, BERT, and Claude Series, etc. Let’s delve into the role of transformers in NLP and elucidate the process of training LLMs using this innovative architecture. appeared first on MarkTechPost.

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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.

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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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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.

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Towards AI

MARCH 3, 2025

Early foundations of NLP were established by statistical and rule-based models like the Bag of Words (BoW). In this article, we will discuss what BoW is and how Transformers revolutionized the field of NLP over time. It is one of the widely used technique in NLP despite its simplicity. Transformer Architecture (Vaswani et al.

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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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Explosion

JANUARY 12, 2018

spaCy In 2017 spaCy grew into one of the most popular open-source libraries for Artificial Intelligence. In April 2017, we published a follow up that described the solution we were working on, and in August we introduced Prodigy , and started accepting beta users. spaCy’s Machine Learning library for NLP in Python.

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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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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.

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ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Researchers and practitioners explored complex architectures, from transformers to reinforcement learning , leading to a surge in sessions on natural language processing (NLP) and computervision.

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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.

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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.

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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.

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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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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).

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Marktechpost

SEPTEMBER 17, 2024

Large language models (LLMs) have seen remarkable success in natural language processing (NLP). Moreover, other methods have been explored for utilizing pre-trained language models in NLP tasks, contributing to the ongoing advancements in the field. However, they pose major challenges in computational resources and memory usage.

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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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Marktechpost

MAY 28, 2024

The Generative Pre-trained Transformer (GPT) series, developed by OpenAI, has revolutionized the field of NLP with its groundbreaking advancements in language generation and understanding. in 2017 , which relies on self-attention mechanisms to process input data in parallel, enhancing computational efficiency and scalability.

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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.

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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.

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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.

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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.)

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Unite.AI

JUNE 27, 2024

AI Categories in CRE Colliers has identified six primary categories of AI that are currently being utilized or expected to be adopted soon: Natural Language Processing (NLP) – Understands, generates, and interacts with human language. According to the data: 33% plan to implement AI within the next two years. billion 2023 : $29.1

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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 (..)

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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.

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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.

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Explosion

AUGUST 22, 2017

#ML — Jason Eisner (@adveisner) August 12, 2017 E.g., regularize toward word embeddings θ that were pretrained on big data for some other objective. — Jason Eisner (@adveisner) August 12, 2017 I have this in the book btw (p. Coeff controls bias/variance tradeoff.

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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.

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Unite.AI

FEBRUARY 8, 2024

So, In 2017, we took our national intelligence agency experience and began to make this happen with the mission of helping organizations prevent breaches, by continuously mapping their external exposure blind spots and finding the paths of least resistance into their internal networks.

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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. ?

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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.

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Sebastian Ruder

AUGUST 23, 2021

Over the last years, models in NLP have become much more powerful, driven by advances in transfer learning. This post aims to give an overview of challenges and opportunities in benchmarking in NLP, together with some general recommendations. Does this mean that we have solved natural language processing? Far from it.

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