Google Natural Language - Revision history

BPeat at 01:07, 10 July 2023

2023-07-10T01:07:09Z

BPeat at 01:34, 18 May 2023

2023-05-18T01:34:27Z

BPeat at 19:13, 28 April 2023

2023-04-28T19:13:04Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T00:25:20Z

Text replacement - "http:" to "https:"

BPeat at 00:06, 28 September 2020

2020-09-28T00:06:54Z

BPeat at 13:05, 13 December 2019

2019-12-13T13:05:41Z

BPeat at 13:04, 13 December 2019

2019-12-13T13:04:17Z

BPeat at 12:57, 13 December 2019

2019-12-13T12:57:18Z

BPeat at 12:53, 21 April 2019

2019-04-21T12:53:03Z

BPeat at 12:42, 21 April 2019

2019-04-21T12:42:30Z

@@ Line 16: / Line 16: @@
 * [[Google]]
-<b>AutoML Natural Language</b>: Support for tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs. By way of refresher, AutoML Natural Language taps machine learning to reveal the structure and meaning of text from emails, chat logs, social media posts, and more. It can extract information about people, places, and events both from uploaded and pasted text or Google Cloud Storage documents, and it allows users to train their own custom AI models to classify, detect, and analyze things like sentiment, entities, content, and syntax. It furthermore offers custom entity extraction, which enables the identification of domain-specific entities within documents that don’t appear in standard language models. AutoML Natural Language has over 5,000 classification labels and allows training on up to 1 million documents up to 10MB in size, which Google says makes it an excellent fit for “complex” use cases like comprehending legal files or document segmentation for organizations with large content taxonomies. It has been improved in the months since its reveal, specifically in the areas of text and document entity extraction — Google says that AutoML Natural Language now considers additional [[context]] (such as the spatial structure and layout information of a document) for model training and prediction to improve the recognition of text in invoices, receipts, resumes, and contracts. Additionally, Google says that AutoML Natural Language is now FedRAMP-authorized at the Moderate level, meaning it has been vetted according to U.S. government specifications for data where the impact of loss is limited or serious. It says that this — along with newly introduced functionality that lets customers create a data set, train a model, and make predictions while keeping the data and related machine learning processing within a single server region — makes it easier for federal agencies to take advantage. [https://venturebeat.com/2019/12/12/google-launches-automl-natural-language-with-improved-text-classification-and-model-training/ Google launches AutoML Natural Language with improved text classification and model training | Kyle Wiggers - VentueBeat]
+<b>AutoML Natural Language</b>: Support for tasks like classification, [[Sentiment Analysis]], and entity extraction, as well as a range of file formats, including native and scanned PDFs. By way of refresher, AutoML Natural Language taps machine learning to reveal the structure and meaning of text from emails, chat logs, social media posts, and more. It can extract information about people, places, and events both from uploaded and pasted text or Google Cloud Storage documents, and it allows users to train their own custom AI models to classify, detect, and analyze things like sentiment, entities, content, and syntax. It furthermore offers custom entity extraction, which enables the identification of domain-specific entities within documents that don’t appear in standard language models. AutoML Natural Language has over 5,000 classification labels and allows training on up to 1 million documents up to 10MB in size, which Google says makes it an excellent fit for “complex” use cases like comprehending legal files or document segmentation for organizations with large content taxonomies. It has been improved in the months since its reveal, specifically in the areas of text and document entity extraction — Google says that AutoML Natural Language now considers additional [[context]] (such as the spatial structure and layout information of a document) for model training and prediction to improve the recognition of text in invoices, receipts, resumes, and contracts. Additionally, Google says that AutoML Natural Language is now FedRAMP-authorized at the Moderate level, meaning it has been vetted according to U.S. government specifications for data where the impact of loss is limited or serious. It says that this — along with newly introduced functionality that lets customers create a data set, train a model, and make predictions while keeping the data and related machine learning processing within a single server region — makes it easier for federal agencies to take advantage. [https://venturebeat.com/2019/12/12/google-launches-automl-natural-language-with-improved-text-classification-and-model-training/ Google launches AutoML Natural Language with improved text classification and model training | Kyle Wiggers - VentueBeat]
 <youtube>ajmWGxAr9Wk</youtube>

@@ Line 16: / Line 16: @@
 * [[Google]]
-<b>AutoML Natural Language</b>: Support for tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs. By way of refresher, AutoML Natural Language taps machine learning to reveal the structure and meaning of text from emails, chat logs, social media posts, and more. It can extract information about people, places, and events both from uploaded and pasted text or Google Cloud Storage documents, and it allows users to train their own custom AI models to classify, detect, and analyze things like sentiment, entities, content, and syntax. It furthermore offers custom entity extraction, which enables the identification of domain-specific entities within documents that don’t appear in standard language models. AutoML Natural Language has over 5,000 classification labels and allows training on up to 1 million documents up to 10MB in size, which Google says makes it an excellent fit for “complex” use cases like comprehending legal files or document segmentation for organizations with large content taxonomies. It has been improved in the months since its reveal, specifically in the areas of text and document entity extraction — Google says that AutoML Natural Language now considers additional context (such as the spatial structure and layout information of a document) for model training and prediction to improve the recognition of text in invoices, receipts, resumes, and contracts. Additionally, Google says that AutoML Natural Language is now FedRAMP-authorized at the Moderate level, meaning it has been vetted according to U.S. government specifications for data where the impact of loss is limited or serious. It says that this — along with newly introduced functionality that lets customers create a data set, train a model, and make predictions while keeping the data and related machine learning processing within a single server region — makes it easier for federal agencies to take advantage. [https://venturebeat.com/2019/12/12/google-launches-automl-natural-language-with-improved-text-classification-and-model-training/ Google launches AutoML Natural Language with improved text classification and model training | Kyle Wiggers - VentueBeat]
+<b>AutoML Natural Language</b>: Support for tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs. By way of refresher, AutoML Natural Language taps machine learning to reveal the structure and meaning of text from emails, chat logs, social media posts, and more. It can extract information about people, places, and events both from uploaded and pasted text or Google Cloud Storage documents, and it allows users to train their own custom AI models to classify, detect, and analyze things like sentiment, entities, content, and syntax. It furthermore offers custom entity extraction, which enables the identification of domain-specific entities within documents that don’t appear in standard language models. AutoML Natural Language has over 5,000 classification labels and allows training on up to 1 million documents up to 10MB in size, which Google says makes it an excellent fit for “complex” use cases like comprehending legal files or document segmentation for organizations with large content taxonomies. It has been improved in the months since its reveal, specifically in the areas of text and document entity extraction — Google says that AutoML Natural Language now considers additional [[context]] (such as the spatial structure and layout information of a document) for model training and prediction to improve the recognition of text in invoices, receipts, resumes, and contracts. Additionally, Google says that AutoML Natural Language is now FedRAMP-authorized at the Moderate level, meaning it has been vetted according to U.S. government specifications for data where the impact of loss is limited or serious. It says that this — along with newly introduced functionality that lets customers create a data set, train a model, and make predictions while keeping the data and related machine learning processing within a single server region — makes it easier for federal agencies to take advantage. [https://venturebeat.com/2019/12/12/google-launches-automl-natural-language-with-improved-text-classification-and-model-training/ Google launches AutoML Natural Language with improved text classification and model training | Kyle Wiggers - VentueBeat]
 <youtube>ajmWGxAr9Wk</youtube>

@@ Line 13: / Line 13: @@
 *** [[Google Cloud Natural Language API]]
 * [[Document Understanding AI]] | Google
-* [[Natural Language Tools & Services]]
+* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 * [[Google]]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Google+nlp+natural+language+Document+Understanding+AI Youtube search...] |
+[https://www.youtube.com/results?search_query=Google+nlp+natural+language+Document+Understanding+AI Youtube search...] |
-[http://www.google.com/search?q=Google+nlp+natural+language+Document+Understanding+AI ...Google search]
+[https://www.google.com/search?q=Google+nlp+natural+language+Document+Understanding+AI ...Google search]
-* [http://cloud.google.com/natural-language/automl/docs/ Google Cloud AutoML Natural Language | Google]
+* [https://cloud.google.com/natural-language/automl/docs/ Google Cloud AutoML Natural Language | Google]
 ** [[Algorithm Administration#Automated Learning|Automated Learning]]
-** [http://cloud.google.com/natural-language/ Cloud Natural Language (NL) | Google]
+** [https://cloud.google.com/natural-language/ Cloud Natural Language (NL) | Google]
 *** [[Google Cloud Natural Language API]]
 * [[Document Understanding AI]] | Google
@@ Line 17: / Line 17: @@
 * [[Google]]
-<b>AutoML Natural Language</b>: Support for tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs. By way of refresher, AutoML Natural Language taps machine learning to reveal the structure and meaning of text from emails, chat logs, social media posts, and more. It can extract information about people, places, and events both from uploaded and pasted text or Google Cloud Storage documents, and it allows users to train their own custom AI models to classify, detect, and analyze things like sentiment, entities, content, and syntax. It furthermore offers custom entity extraction, which enables the identification of domain-specific entities within documents that don’t appear in standard language models. AutoML Natural Language has over 5,000 classification labels and allows training on up to 1 million documents up to 10MB in size, which Google says makes it an excellent fit for “complex” use cases like comprehending legal files or document segmentation for organizations with large content taxonomies. It has been improved in the months since its reveal, specifically in the areas of text and document entity extraction — Google says that AutoML Natural Language now considers additional context (such as the spatial structure and layout information of a document) for model training and prediction to improve the recognition of text in invoices, receipts, resumes, and contracts. Additionally, Google says that AutoML Natural Language is now FedRAMP-authorized at the Moderate level, meaning it has been vetted according to U.S. government specifications for data where the impact of loss is limited or serious. It says that this — along with newly introduced functionality that lets customers create a data set, train a model, and make predictions while keeping the data and related machine learning processing within a single server region — makes it easier for federal agencies to take advantage. [http://venturebeat.com/2019/12/12/google-launches-automl-natural-language-with-improved-text-classification-and-model-training/ Google launches AutoML Natural Language with improved text classification and model training | Kyle Wiggers - VentueBeat]
+<b>AutoML Natural Language</b>: Support for tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs. By way of refresher, AutoML Natural Language taps machine learning to reveal the structure and meaning of text from emails, chat logs, social media posts, and more. It can extract information about people, places, and events both from uploaded and pasted text or Google Cloud Storage documents, and it allows users to train their own custom AI models to classify, detect, and analyze things like sentiment, entities, content, and syntax. It furthermore offers custom entity extraction, which enables the identification of domain-specific entities within documents that don’t appear in standard language models. AutoML Natural Language has over 5,000 classification labels and allows training on up to 1 million documents up to 10MB in size, which Google says makes it an excellent fit for “complex” use cases like comprehending legal files or document segmentation for organizations with large content taxonomies. It has been improved in the months since its reveal, specifically in the areas of text and document entity extraction — Google says that AutoML Natural Language now considers additional context (such as the spatial structure and layout information of a document) for model training and prediction to improve the recognition of text in invoices, receipts, resumes, and contracts. Additionally, Google says that AutoML Natural Language is now FedRAMP-authorized at the Moderate level, meaning it has been vetted according to U.S. government specifications for data where the impact of loss is limited or serious. It says that this — along with newly introduced functionality that lets customers create a data set, train a model, and make predictions while keeping the data and related machine learning processing within a single server region — makes it easier for federal agencies to take advantage. [https://venturebeat.com/2019/12/12/google-launches-automl-natural-language-with-improved-text-classification-and-model-training/ Google launches AutoML Natural Language with improved text classification and model training | Kyle Wiggers - VentueBeat]
 <youtube>ajmWGxAr9Wk</youtube>

@@ Line 9: / Line 9: @@
 * [http://cloud.google.com/natural-language/automl/docs/ Google Cloud AutoML Natural Language | Google]
-** [[Automated Machine Learning (AML) - AutoML]]
+** [[Algorithm Administration#Automated Learning|Automated Learning]]
 ** [http://cloud.google.com/natural-language/ Cloud Natural Language (NL) | Google]
 *** [[Google Cloud Natural Language API]]

← Older revision		Revision as of 12:53, 21 April 2019
Line 18:		Line 18:
	* [[Google]]		* [[Google]]

−	<youtube>~~Y2wgQjxrPD8~~</youtube>	+	<youtube>ajmWGxAr9Wk</youtube>