Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://aka.ms/azsdk-python-textanalytics-ref-docs below:

azure.ai.textanalytics package | Microsoft Learn

An object representing a single summary with context for given document.

New in version 2023-04-01: The AbstractiveSummary model.

AbstractiveSummaryAction encapsulates the parameters for starting a long-running abstractive summarization operation. For a conceptual discussion of extractive summarization, see the service documentation: https://learn.microsoft.com/azure/cognitive-services/language-service/summarization/overview

Abstractive summarization generates a summary for the input documents. Abstractive summarization is different from extractive summarization in that extractive summarization is the strategy of concatenating extracted sentences from the input document into a summary, while abstractive summarization involves paraphrasing the document using novel sentences.

New in version 2023-04-01: The AbstractiveSummaryAction model.

AbstractiveSummaryResult is a result object which contains the summary generated for a particular document.

New in version 2023-04-01: The AbstractiveSummaryResult model.

AnalyzeHealthcareEntitiesAction encapsulates the parameters for starting a long-running healthcare entities analysis operation.

If you just want to analyze healthcare entities in a list of documents, and not perform multiple long running actions on the input of documents, call method begin_analyze_healthcare_entities instead of interfacing with this model.

New in version 2022-05-01: The AnalyzeHealthcareEntitiesAction model.

AnalyzeHealthcareEntitiesResult contains the Healthcare entities from a particular document.

AnalyzeSentimentAction encapsulates the parameters for starting a long-running Sentiment Analysis operation.

If you just want to analyze sentiment in a list of documents, and not perform multiple long running actions on the input of documents, call method analyze_sentiment instead of interfacing with this model.

AnalyzeSentimentResult is a result object which contains the overall predicted sentiment and confidence scores for your document and a per-sentence sentiment prediction with scores.

AssessmentSentiment contains the predicted sentiment, confidence scores and other information about an assessment given about a particular target. For example, in the sentence "The food is good", the assessment of the target 'food' is 'good'.

CategorizedEntity contains information about a particular entity found in text.

New in version v3.1: The offset and length properties.

ClassificationCategory represents a classification of the input document.

ClassifyDocumentResult is a result object which contains the classifications for a particular document.

The input document to be analyzed for detecting language.

DetectLanguageResult is a result object which contains the detected language of a particular document.

DetectedLanguage contains the predicted language found in text, its confidence score, and its ISO 639-1 representation.

DocumentError is an error object which represents an error on the individual document.

ExtractKeyPhrasesAction encapsulates the parameters for starting a long-running key phrase extraction operation

If you just want to extract key phrases from a list of documents, and not perform multiple long running actions on the input of documents, call method extract_key_phrases instead of interfacing with this model.

ExtractKeyPhrasesResult is a result object which contains the key phrases found in a particular document.

ExtractiveSummaryAction encapsulates the parameters for starting a long-running Extractive Text Summarization operation. For a conceptual discussion of extractive summarization, see the service documentation: https://learn.microsoft.com/azure/cognitive-services/language-service/summarization/overview

New in version 2023-04-01: The ExtractiveSummaryAction model.

ExtractiveSummaryResult is a result object which contains the extractive text summarization from a particular document.

HealthcareEntity contains information about a Healthcare entity found in text.

Contains various assertions about a HealthcareEntity.

For example, if an entity is a diagnosis, is this diagnosis 'conditional' on a symptom? Are the doctors 'certain' about this diagnosis? Is this diagnosis 'associated' with another diagnosis?

HealthcareEntityDataSource contains information representing an entity reference in a known data source.

HealthcareRelation is a result object which represents a relation detected in a document.

Every HealthcareRelation is an entity graph of a certain relation type, where all entities are connected and have specific roles within the relation context.

New in version 2023-04-01: The confidence_score property.

A model representing a role in a relation.

For example, in "The subject took 100 mg of ibuprofen", "100 mg" is a dosage entity fulfilling the role "Dosage" in the extracted relation "DosageOfMedication".

LinkedEntity contains a link to the well-known recognized entity in text. The link comes from a data source like Wikipedia or Bing. It additionally includes all of the matches of this entity found in the document.

New in version v3.1: The bing_entity_search_api_id property.

A match for the linked entity found in text. Provides the confidence score of the prediction and where the entity was found in the text.

New in version v3.1: The offset and length properties.

A mined opinion object represents an opinion we've extracted from a sentence. It consists of both a target that these opinions are about, and the assessments representing the opinion.

MultiLabelClassifyAction encapsulates the parameters for starting a long-running custom multi label classification operation. For information on regional support of custom features and how to train a model to classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities

New in version 2022-05-01: The MultiLabelClassifyAction model.

PiiEntity contains information about a Personally Identifiable Information (PII) entity found in text.

RecognizeCustomEntitiesAction encapsulates the parameters for starting a long-running custom entity recognition operation. For information on regional support of custom features and how to train a model to recognize custom entities, see https://aka.ms/azsdk/textanalytics/customentityrecognition

New in version 2022-05-01: The RecognizeCustomEntitiesAction model.

RecognizeCustomEntitiesResult is a result object which contains the custom recognized entities from a particular document.

RecognizeEntitiesAction encapsulates the parameters for starting a long-running Entities Recognition operation.

If you just want to recognize entities in a list of documents, and not perform multiple long running actions on the input of documents, call method recognize_entities instead of interfacing with this model.

RecognizeEntitiesResult is a result object which contains the recognized entities from a particular document.

RecognizeLinkedEntitiesAction encapsulates the parameters for starting a long-running Linked Entities Recognition operation.

If you just want to recognize linked entities in a list of documents, and not perform multiple long running actions on the input of documents, call method recognize_linked_entities instead of interfacing with this model.

RecognizeLinkedEntitiesResult is a result object which contains links to a well-known knowledge base, like for example, Wikipedia or Bing.

RecognizePiiEntitiesAction encapsulates the parameters for starting a long-running PII Entities Recognition operation. See more information in the service docs: https://aka.ms/azsdk/language/pii

If you just want to recognize pii entities in a list of documents, and not perform multiple long running actions on the input of documents, call method recognize_pii_entities instead of interfacing with this model.

RecognizePiiEntitiesResult is a result object which contains the recognized Personally Identifiable Information (PII) entities from a particular document.

SentenceSentiment contains the predicted sentiment and confidence scores for each individual sentence in the document.

New in version v3.1: The offset, length, and mined_opinions properties.

The confidence scores (Softmax scores) between 0 and 1. Higher values indicate higher confidence.

SingleLabelClassifyAction encapsulates the parameters for starting a long-running custom single label classification operation. For information on regional support of custom features and how to train a model to classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities

New in version 2022-05-01: The SingleLabelClassifyAction model.

The context of the summary.

New in version 2023-04-01: The SummaryContext model.

Represents a single sentence from the extractive text summarization.

New in version 2023-04-01: The SummarySentence model.

TargetSentiment contains the predicted sentiment, confidence scores and other information about a key component of a product/service. For example in "The food at Hotel Foo is good", "food" is an key component of "Hotel Foo".

Implements a protocol which returned poller objects are consistent with.

The Language service API is a suite of natural language processing (NLP) skills built with the best-in-class Microsoft machine learning algorithms. The API can be used to analyze unstructured text for tasks such as sentiment analysis, key phrase extraction, entities recognition, and language detection, and more.

Further documentation can be found in https://docs.microsoft.com/azure/cognitive-services/language-service/overview

TextAnalyticsError contains the error code, message, and other details that explain why the batch or individual document failed to be processed by the service.

TextAnalyticsWarning contains the warning code and message that explains why the response has a warning.

TextDocumentBatchStatistics contains information about the request payload. Note: This object is not returned in the response and needs to be retrieved by a response hook.

The input document to be analyzed by the service.

TextDocumentStatistics contains information about the document payload.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4