Hal Daumé III

AUGUST 5, 2016

About a month ago, the paper Bag of Tricks for Efficient Text Categorization was posted to arxiv. See this tutorial for more on how to do NLP in VW.) At the time, I said if they gave me the data, I'd run vw on it and report results. They were nice enough to share the data but I never got around to running it. This took 2.4s

Categorization 40

Categorization NLP 40

Probably Approximately a Scientific Blog

JANUARY 12, 2021

The release of Google Translate’s neural models in 2016 reported large performance improvements: “60% reduction in translation errors on several popular language pairs”. In NLP, dialogue systems generate highly generic responses such as “I don’t know” even for simple questions. Open-ended generation is prone to repetition.

Natural Language Processing 52

Natural Language Processing BERT NLP Neural Network 52

Explosion

NOVEMBER 9, 2016

Most NLP problems can be reduced to machine learning problems that take one or more texts as input. However, most NLP problems require understanding of longer spans of text, not just individual words. This has always been a huge weakness of NLP models. 2016) presented a model that achieved 86.8% Now we have a solution.

Deep Learning 52

Deep Learning NLP Convolutional Neural Networks Neural Network 52

Defined.ai blog

APRIL 10, 2023

Named Entity Recognition (NER) is a natural language processing (NLP) subtask that involves automatically identifying and categorizing named entities mentioned in a text, such as people, organizations, locations, dates, and other proper nouns.

NLP 52

NLP Categorization Natural Language Processing Algorithm 52

Defined.ai blog

APRIL 10, 2023

Named Entity Recognition (NER) is a natural language processing (NLP) subtask that involves automatically identifying and categorizing named entities mentioned in a text, such as people, organizations, locations, dates, and other proper nouns.

NLP 52

NLP Categorization Natural Language Processing Algorithm 52

Snorkel AI

MARCH 1, 2023

2016) This paper introduced DCGANs, a type of generative model that uses convolutional neural networks to generate images with high fidelity. The company is best known for NLP tools, but also enables the use of computer vision, audio, and multimodal models. Attention Is All You Need Vaswani et al.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

AWS Machine Learning Blog

FEBRUARY 28, 2023

Use natural language processing (NLP) in Amazon HealthLake to extract non-sensitive data from unstructured blobs. We can see that Amazon HeathLake NLP interprets this as containing the condition “stroke” by querying for the condition record that has the same patient ID and displays “stroke.” mg/actuat / salmeterol 0.05

ML 75

ML Machine Learning Categorization NLP 75

Unite.AI

SEPTEMBER 20, 2023

Transformers in NLP In 2017, Cornell University published an influential paper that introduced transformers. These are deep learning models used in NLP. Hugging Face , started in 2016, aims to make NLP models accessible to everyone. It is based in New York and was founded in 2016."

LLM 342

LLM BERT NLP Python 342

Explosion

FEBRUARY 12, 2017

The question of how idealised NLP experiments should be is not new. The model is implemented using Thinc , a small library of NLP-optimized machine learning functions being developed for use in spaCy. We want to learn a single categorical label for the pair of questions, so we want to get a single vector for the pair of sentences.

Neural Network 40

Neural Network Deep Learning Natural Language Processing NLP 40

Probably Approximately a Scientific Blog

NOVEMBER 23, 2016

It's a Bird, It's a Plane, It's Superman (not antonyms) Many people would categorize a pair of words as opposites if they represent two mutually exclusive options/entities in the world, like male and female. CogALex 2016. black and white , and tuna and salmon. Enrico Santus, Qin Lu, Alessandro Lenci, Chu-Ren Huang. PACLIC 2014. [6]

NLP 52

NLP Natural Language Processing Categorization 52