Unite.AI

JUNE 25, 2023

.” Incorporating Physics into Computer Vision AI The research team outlines three innovative ways to integrate physics into computer vision AI: Infusing physics into AI data sets: This involves tagging objects with additional information, such as their potential speed or weight, akin to characters in video games.

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Computer Vision Robotics Neural Network Hybrid AI 361

John Snow Labs

JUNE 7, 2023

For example, a named entity recognizer annotator might identify and tag entities such as people, organizations, and locations in a text document, while a sentiment analysis annotator might classify the sentiment of the text as positive, negative, or neutral. John is 20 years old and Peter is 26" data = spark.createDataFrame([[text]]).toDF("text")

NLP 52

NLP Natural Language Processing Metadata Python 52

AWS Machine Learning Blog

MARCH 6, 2024

These can be represented by letters of the alphabet, which then allows us to analyze and explore proteins as a text string. Despite this variety, all proteins are made of repeating chains of molecules called amino acids. The human genome encodes 20 standard amino acids, each with a slightly different chemical structure.

Neural Network 87

Neural Network Large Language Models Machine Learning ML 87

John Snow Labs

JUNE 5, 2023

It is a critical step in several natural language processing (NLP) tasks because many NLP tasks take sentence as an input unit, such as part-of-speech tagging, dependency parsing, named entity recognition or machine translation. One problem caused by this is that they cannot accommodate polysemy.

NLP 52

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Probably Approximately a Scientific Blog

AUGUST 10, 2015

Other tasks require multi-class classification , in which every instance can be classified to one of several predefined classes; for instance, in optical character recognition (OCR), each hand-written character should be classified as one of the possible characters or digits in the alphabet. noun, verb, adjective).

Machine Learning 52

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Allen AI

AUGUST 18, 2023

In practice: We use fasttext’s language identification models to tag content by language. Our approach relies on a combination of logistic classifiers (content tagging) and regular expressions (PII detection). We use a fairly permissive threshold, keeping documents that have a likelihood over 50% of being in English.

Auto-complete 98

Auto-complete Large Language Models NLP Explainability 98

Explosion

SEPTEMBER 17, 2013

There are a tonne of “best known techniques” for POS tagging, and you should ignore the others and just use Averaged Perceptron. You should use two tags of history, and features derived from the Brown word clusters distributed here. About 50% of the words can be tagged that way.

Python 40

Python Algorithm NLP Natural Language Processing 40