Detect Entities

comprehend_detect_entities

R Documentation

Detects named entities in input text when you use the pre-trained model

Description

Detects named entities in input text when you use the pre-trained model. Detects custom entities if you have a custom entity recognition model.

When detecting named entities using the pre-trained model, use plain text as the input. For more information about named entities, see Entities in the Comprehend Developer Guide.

When you use a custom entity recognition model, you can input plain text or you can upload a single-page input document (text, PDF, Word, or image).

If the system detects errors while processing a page in the input document, the API response includes an entry in Errors for each error.

If the system detects a document-level error in your input document, the API returns an InvalidRequestException error response. For details about this exception, see Errors in semi-structured documents in the Comprehend Developer Guide.

Usage

comprehend_detect_entities(Text, LanguageCode, EndpointArn, Bytes,
  DocumentReaderConfig)

Arguments

Text

A UTF-8 text string. The maximum string size is 100 KB. If you enter text using this parameter, do not use the Bytes parameter.

LanguageCode

The language of the input documents. You can specify any of the primary languages supported by Amazon Comprehend. If your request includes the endpoint for a custom entity recognition model, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you specify here.

All input documents must be in the same language.

EndpointArn

The Amazon Resource Name of an endpoint that is associated with a custom entity recognition model. Provide an endpoint if you want to detect entities by using your own custom model instead of the default model that is used by Amazon Comprehend.

If you specify an endpoint, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you provide in your request.

For information about endpoints, see Managing endpoints.

Bytes

This field applies only when you use a custom entity recognition model that was trained with PDF annotations. For other cases, enter your text input in the Text field.

Use the Bytes parameter to input a text, PDF, Word or image file. Using a plain-text file in the Bytes parameter is equivelent to using the Text parameter (the Entities field in the response is identical).

You can also use the Bytes parameter to input an Amazon Textract DetectDocumentText or AnalyzeDocument output file.

Provide the input document as a sequence of base64-encoded bytes. If your code uses an Amazon Web Services SDK to detect entities, the SDK may encode the document file bytes for you.

The maximum length of this field depends on the input document type. For details, see Inputs for real-time custom analysis in the Comprehend Developer Guide.

If you use the Bytes parameter, do not use the Text parameter.

DocumentReaderConfig

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

Value

A list with the following syntax:

list(
  Entities = list(
    list(
      Score = 123.0,
      Type = "PERSON"|"LOCATION"|"ORGANIZATION"|"COMMERCIAL_ITEM"|"EVENT"|"DATE"|"QUANTITY"|"TITLE"|"OTHER",
      Text = "string",
      BeginOffset = 123,
      EndOffset = 123,
      BlockReferences = list(
        list(
          BlockId = "string",
          BeginOffset = 123,
          EndOffset = 123,
          ChildBlocks = list(
            list(
              ChildBlockId = "string",
              BeginOffset = 123,
              EndOffset = 123
            )
          )
        )
      )
    )
  ),
  DocumentMetadata = list(
    Pages = 123,
    ExtractedCharacters = list(
      list(
        Page = 123,
        Count = 123
      )
    )
  ),
  DocumentType = list(
    list(
      Page = 123,
      Type = "NATIVE_PDF"|"SCANNED_PDF"|"MS_WORD"|"IMAGE"|"PLAIN_TEXT"|"TEXTRACT_DETECT_DOCUMENT_TEXT_JSON"|"TEXTRACT_ANALYZE_DOCUMENT_JSON"
    )
  ),
  Blocks = list(
    list(
      Id = "string",
      BlockType = "LINE"|"WORD",
      Text = "string",
      Page = 123,
      Geometry = list(
        BoundingBox = list(
          Height = 123.0,
          Left = 123.0,
          Top = 123.0,
          Width = 123.0
        ),
        Polygon = list(
          list(
            X = 123.0,
            Y = 123.0
          )
        )
      ),
      Relationships = list(
        list(
          Ids = list(
            "string"
          ),
          Type = "CHILD"
        )
      )
    )
  ),
  Errors = list(
    list(
      Page = 123,
      ErrorCode = "TEXTRACT_BAD_PAGE"|"TEXTRACT_PROVISIONED_THROUGHPUT_EXCEEDED"|"PAGE_CHARACTERS_EXCEEDED"|"PAGE_SIZE_EXCEEDED"|"INTERNAL_SERVER_ERROR",
      ErrorMessage = "string"
    )
  )
)

Request syntax

svc$detect_entities(
  Text = "string",
  LanguageCode = "en"|"es"|"fr"|"de"|"it"|"pt"|"ar"|"hi"|"ja"|"ko"|"zh"|"zh-TW",
  EndpointArn = "string",
  Bytes = raw,
  DocumentReaderConfig = list(
    DocumentReadAction = "TEXTRACT_DETECT_DOCUMENT_TEXT"|"TEXTRACT_ANALYZE_DOCUMENT",
    DocumentReadMode = "SERVICE_DEFAULT"|"FORCE_DOCUMENT_READ_ACTION",
    FeatureTypes = list(
      "TABLES"|"FORMS"
    )
  )
)