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

JUNE 2, 2023

Sentiment analysis is a popular natural language processing (NLP) task that involves determining the sentiment of a given text, whether it is positive, negative, or neutral. An annotator takes an input text document and produces an output document with additional metadata, which can be used for further processing or analysis.

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Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). Improving Neural Language Models with a Continuous Cache Edouard Grave, Armand Joulin, Nicolas Usunier. ArXiv 2017.

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John Snow Labs

MAY 26, 2023

Sentence embeddings are a powerful tool in natural language processing that helps analyze and understand language. An annotator takes an input text document and produces an output document with additional metadata, which can be used for further processing or analysis. setInputCols(["sentence"]).setOutputCol("sentence_bert_embeddings").setCaseSensitive(True).setMaxSentenceLength(512)

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DataRobot Blog

MARCH 9, 2022

This is one of the reasons why detecting sentiment from natural language (NLP or natural language processing ) is a surprisingly complex task. Some common datasets include the SemEval 2007 Task 14 , EmoBank , WASSA 2017 , The Emotion in Text Dataset , and the Affect Dataset.

Machine Learning 52

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John Snow Labs

JUNE 6, 2023

Many different transformer models have already been implemented in Spark NLP, and specifically for text classification, Spark NLP provides various annotators that are designed to work with pretrained language models. This sequence can be made up of text, as well as other types of data like audio, images, or time series.

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John Snow Labs

JUNE 7, 2023

Normalization refers to the process of converting text to a standard format, which can help improve the accuracy and efficiency of subsequent natural language processing (NLP) tasks. It is a configurable component that can be customized based on specific use cases and requirements.

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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. Sentiment analysis is an automated process capable of understanding the feelings or opinions that underlie a text.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Thirdly, the presence of GPUs enabled the labeled data to be processed. In 2017, the landmark paper “ Attention is all you need ” was published, which laid out a new deep learning architecture based on the transformer. The following table shows the metadata of three of the largest accelerated compute instances.

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Sebastian Ruder

JUNE 6, 2022

We can also incorporate additional knowledge by modifying the training data, e.g., by inserting metadata strings (e.g., To this end, we need to be able to extract, contextualize, and scope relevant information and provide explanations for the reasoning process. use an LM to generate relevant knowledge statements in a few-shot setting.

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Snorkel AI

JANUARY 20, 2023

In 2017 alone, there were about 4.8 This is the post text and generally, you also find structured information, like the location, and some metadata, like the phone number (present here—I’ve actually redacted it because it’s personally identifiable information), email, websites, user information, and so on.

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