Module aws_sdk_comprehend::types

Data structures used by operation inputs/outputs.

Modules

• builders
  Builders
• error
  Error types that Amazon Comprehend can respond with.

Structs

• AugmentedManifestsListItem

  An augmented manifest file that provides training data for your custom model. An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth.

• BatchDetectDominantLanguageItemResult

  The result of calling the operation. The operation returns one object for each document that is successfully processed by the operation.

• BatchDetectEntitiesItemResult

  The result of calling the operation. The operation returns one object for each document that is successfully processed by the operation.

• BatchDetectKeyPhrasesItemResult

  The result of calling the operation. The operation returns one object for each document that is successfully processed by the operation.

• BatchDetectSentimentItemResult

  The result of calling the operation. The operation returns one object for each document that is successfully processed by the operation.

• BatchDetectSyntaxItemResult

  The result of calling the operation. The operation returns one object that is successfully processed by the operation.

• BatchDetectTargetedSentimentItemResult

  Analysis results for one of the documents in the batch.

• BatchItemError

  Describes an error that occurred while processing a document in a batch. The operation returns on BatchItemError object for each document that contained an error.

• Block

  Information about each word or line of text in the input document.

• BlockReference

  A reference to a block.

• BoundingBox

  The bounding box around the detected page or around an element on a document page. The left (x-coordinate) and top (y-coordinate) are coordinates that represent the top and left sides of the bounding box. Note that the upper-left corner of the image is the origin (0,0).

• ChildBlock

  Nested block contained within a block.

• ClassifierEvaluationMetrics

  Describes the result metrics for the test data associated with an documentation classifier.

• ClassifierMetadata

  Provides information about a document classifier.

• DataSecurityConfig

  Data security configuration.

• DatasetAugmentedManifestsListItem

  An augmented manifest file that provides training data for your custom model. An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth.

• DatasetDocumentClassifierInputDataConfig

  Describes the dataset input data configuration for a document classifier model.

• DatasetEntityRecognizerAnnotations

  Describes the annotations associated with a entity recognizer.

• DatasetEntityRecognizerDocuments

  Describes the documents submitted with a dataset for an entity recognizer model.

• DatasetEntityRecognizerEntityList

  Describes the dataset entity list for an entity recognizer model.

• DatasetEntityRecognizerInputDataConfig

  Specifies the format and location of the input data. You must provide either the Annotations parameter or the EntityList parameter.

• DatasetFilter

  Filter the datasets based on creation time or dataset status.

• DatasetInputDataConfig

  Specifies the format and location of the input data for the dataset.

• DatasetProperties

  Properties associated with the dataset.

• DocumentClass

  Specifies the class that categorizes the document being analyzed

• DocumentClassificationConfig

  Configuration required for a document classification model.

• DocumentClassificationJobFilter

  Provides information for filtering a list of document classification jobs. For more information, see the operation. You can provide only one filter parameter in each request.

• DocumentClassificationJobProperties

  Provides information about a document classification job.

• DocumentClassifierDocuments

  The location of the training documents. This parameter is required in a request to create a semi-structured document classification model.

• DocumentClassifierFilter

  Provides information for filtering a list of document classifiers. You can only specify one filtering parameter in a request. For more information, see the ListDocumentClassifiers operation.

• DocumentClassifierInputDataConfig

  The input properties for training a document classifier.

• DocumentClassifierOutputDataConfig

  Provide the location for output data from a custom classifier job. This field is mandatory if you are training a native document model.

• DocumentClassifierProperties

  Provides information about a document classifier.

• DocumentClassifierSummary

  Describes information about a document classifier and its versions.

• DocumentLabel

  Specifies one of the label or labels that categorize the document being analyzed.

• DocumentMetadata

  Information about the document, discovered during text extraction.

• DocumentReaderConfig

  Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.

• DocumentTypeListItem

  Document type for each page in the document.

• DominantLanguage

  Returns the code for the dominant language in the input text and the level of confidence that Amazon Comprehend has in the accuracy of the detection.

• DominantLanguageDetectionJobFilter

  Provides information for filtering a list of dominant language detection jobs. For more information, see the operation.

• DominantLanguageDetectionJobProperties

  Provides information about a dominant language detection job.

• EndpointFilter

  The filter used to determine which endpoints are returned. You can filter jobs on their name, model, status, or the date and time that they were created. You can only set one filter at a time.

• EndpointProperties

  Specifies information about the specified endpoint. For information about endpoints, see Managing endpoints.

• EntitiesDetectionJobFilter

  Provides information for filtering a list of dominant language detection jobs. For more information, see the operation.

• EntitiesDetectionJobProperties

  Provides information about an entities detection job.

• Entity

  Provides information about an entity.

• EntityLabel

  Specifies one of the label or labels that categorize the personally identifiable information (PII) entity being analyzed.

• EntityRecognitionConfig

  Configuration required for an entity recognition model.

• EntityRecognizerAnnotations

  Describes the annotations associated with a entity recognizer.

• EntityRecognizerDocuments

  Describes the training documents submitted with an entity recognizer.

• EntityRecognizerEntityList

  Describes the entity list submitted with an entity recognizer.

• EntityRecognizerEvaluationMetrics

  Detailed information about the accuracy of an entity recognizer.

• EntityRecognizerFilter

  Provides information for filtering a list of entity recognizers. You can only specify one filtering parameter in a request. For more information, see the ListEntityRecognizers operation./>

• EntityRecognizerInputDataConfig

  Specifies the format and location of the input data.

• EntityRecognizerMetadata

  Detailed information about an entity recognizer.

• EntityRecognizerMetadataEntityTypesListItem

  Individual item from the list of entity types in the metadata of an entity recognizer.

• EntityRecognizerOutputDataConfig

  Output data configuration.

• EntityRecognizerProperties

  Describes information about an entity recognizer.

• EntityRecognizerSummary

  Describes the information about an entity recognizer and its versions.

• EntityTypesEvaluationMetrics

  Detailed information about the accuracy of an entity recognizer for a specific entity type.

• EntityTypesListItem

  An entity type within a labeled training dataset that Amazon Comprehend uses to train a custom entity recognizer.

• ErrorsListItem

  Text extraction encountered one or more page-level errors in the input document.

• EventsDetectionJobFilter

  Provides information for filtering a list of event detection jobs.

• EventsDetectionJobProperties

  Provides information about an events detection job.

• ExtractedCharactersListItem

  Array of the number of characters extracted from each page.

• FlywheelFilter

  Filter the flywheels based on creation time or flywheel status.

• FlywheelIterationFilter

  Filter the flywheel iterations based on creation time.

• FlywheelIterationProperties

  The configuration properties of a flywheel iteration.

• FlywheelModelEvaluationMetrics

  The evaluation metrics associated with the evaluated model.

• FlywheelProperties

  The flywheel properties.

• FlywheelSummary

  Flywheel summary information.

• Geometry

  Information about the location of items on a document page.

• InputDataConfig

  The input properties for an inference job. The document reader config field applies only to non-text inputs for custom analysis.

• InvalidRequestDetail

  Provides additional detail about why the request failed:

• KeyPhrase

  Describes a key noun phrase.

• KeyPhrasesDetectionJobFilter

  Provides information for filtering a list of dominant language detection jobs. For more information, see the operation.

• KeyPhrasesDetectionJobProperties

  Provides information about a key phrases detection job.

• MentionSentiment

  Contains the sentiment and sentiment score for one mention of an entity.

• OutputDataConfig

  Provides configuration parameters for the output of inference jobs.

• PartOfSpeechTag

  Identifies the part of speech represented by the token and gives the confidence that Amazon Comprehend has that the part of speech was correctly identified. For more information about the parts of speech that Amazon Comprehend can identify, see Syntax in the Comprehend Developer Guide.

• PiiEntitiesDetectionJobFilter

  Provides information for filtering a list of PII entity detection jobs.

• PiiEntitiesDetectionJobProperties

  Provides information about a PII entities detection job.

• PiiEntity

  Provides information about a PII entity.

• PiiOutputDataConfig

  Provides configuration parameters for the output of PII entity detection jobs.

• Point

  The X and Y coordinates of a point on a document page.

• RedactionConfig

  Provides configuration parameters for PII entity redaction.

• RelationshipsListItem

  List of child blocks for the current block.

• SentimentDetectionJobFilter

  Provides information for filtering a list of dominant language detection jobs. For more information, see the operation.

• SentimentDetectionJobProperties

  Provides information about a sentiment detection job.

• SentimentScore

  Describes the level of confidence that Amazon Comprehend has in the accuracy of its detection of sentiments.

• SyntaxToken

  Represents a work in the input text that was recognized and assigned a part of speech. There is one syntax token record for each word in the source text.

• Tag

  A key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with the key-value pair ‘Department’:’Sales’ might be added to a resource to indicate its use by a particular department.

• TargetedSentimentDetectionJobFilter

  Provides information for filtering a list of dominant language detection jobs. For more information, see the ListTargetedSentimentDetectionJobs operation.

• TargetedSentimentDetectionJobProperties

  Provides information about a targeted sentiment detection job.

• TargetedSentimentEntity

  Information about one of the entities found by targeted sentiment analysis.

• TargetedSentimentMention

  Information about one mention of an entity. The mention information includes the location of the mention in the text and the sentiment of the mention.

• TaskConfig

  Configuration about the model associated with a flywheel.

• TextSegment

  One of the of text strings. Each string has a size limit of 1KB.

• TopicsDetectionJobFilter

  Provides information for filtering topic detection jobs. For more information, see .

• TopicsDetectionJobProperties

  Provides information about a topic detection job.

• ToxicContent

  Toxic content analysis result for one string. For more information about toxicity detection, see Toxicity detection in the Amazon Comprehend Developer Guide

• ToxicLabels

  Toxicity analysis result for one string. For more information about toxicity detection, see Toxicity detection in the Amazon Comprehend Developer Guide

• UpdateDataSecurityConfig

  Data security configuration.

• VpcConfig

  Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for the job. For more information, see Amazon VPC.

• WarningsListItem

  The system identified one of the following warnings while processing the input document:

Enums

• AugmentedManifestsDocumentTypeFormat
  When writing a match expression against AugmentedManifestsDocumentTypeFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• BlockType
  When writing a match expression against BlockType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DatasetDataFormat
  When writing a match expression against DatasetDataFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DatasetStatus
  When writing a match expression against DatasetStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DatasetType
  When writing a match expression against DatasetType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DocumentClassifierDataFormat
  When writing a match expression against DocumentClassifierDataFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DocumentClassifierDocumentTypeFormat
  When writing a match expression against DocumentClassifierDocumentTypeFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DocumentClassifierMode
  When writing a match expression against DocumentClassifierMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DocumentReadAction
  When writing a match expression against DocumentReadAction, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DocumentReadFeatureTypes
  When writing a match expression against DocumentReadFeatureTypes, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DocumentReadMode
  When writing a match expression against DocumentReadMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DocumentType
  When writing a match expression against DocumentType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• EndpointStatus
  When writing a match expression against EndpointStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• EntityRecognizerDataFormat
  When writing a match expression against EntityRecognizerDataFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• EntityType
  When writing a match expression against EntityType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• FlywheelIterationStatus
  When writing a match expression against FlywheelIterationStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• FlywheelStatus
  When writing a match expression against FlywheelStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• InputFormat
  When writing a match expression against InputFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• InvalidRequestDetailReason
  When writing a match expression against InvalidRequestDetailReason, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• InvalidRequestReason
  When writing a match expression against InvalidRequestReason, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• JobStatus
  When writing a match expression against JobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• LanguageCode
  When writing a match expression against LanguageCode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• ModelStatus
  When writing a match expression against ModelStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• ModelType
  When writing a match expression against ModelType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• PageBasedErrorCode
  When writing a match expression against PageBasedErrorCode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• PageBasedWarningCode
  When writing a match expression against PageBasedWarningCode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• PartOfSpeechTagType
  When writing a match expression against PartOfSpeechTagType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• PiiEntitiesDetectionMaskMode
  When writing a match expression against PiiEntitiesDetectionMaskMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• PiiEntitiesDetectionMode
  When writing a match expression against PiiEntitiesDetectionMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• PiiEntityType
  When writing a match expression against PiiEntityType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• RelationshipType
  When writing a match expression against RelationshipType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• SentimentType
  When writing a match expression against SentimentType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• Split
  When writing a match expression against Split, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• SyntaxLanguageCode
  When writing a match expression against SyntaxLanguageCode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• TargetedSentimentEntityType
  When writing a match expression against TargetedSentimentEntityType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• ToxicContentType
  When writing a match expression against ToxicContentType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.