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Outputs for asynchronous analysis jobs

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Outputs for asynchronous analysis jobs - Amazon Comprehend

After an analysis job completes, it stores the results in the S3 bucket that you specified in the request.

Outputs for text inputs

For text input files, the output consists of a list of entities for each input document.

The following example shows the output for two documents from an input file named 50_docs, using one document per line format.

{ "File": "50_docs", "Line": 0, "Entities": [ { "BeginOffset": 0, "EndOffset": 22, "Score": 0.9763959646224976, "Text": "John Johnson", "Type": "JUDGE" } ] } { "File": "50_docs", "Line": 1, "Entities": [ { "BeginOffset": 11, "EndOffset": 15, "Score": 0.9615424871444702, "Text": "Thomas Kincaid", "Type": "JUDGE" } ] }

Outputs for semi-structured inputs

For semi-structured input documents, the output can include the following additional fields:

  • DocumentMetadata – Extraction information about the document. The metadata includes a list of pages in the document, with the number of characters extracted from each page. This field is present in the response if the request included the Byte parameter.

  • DocumentType – The document type for each page in the input document. This field is present in the response for a request that included the Byte parameter.

  • Blocks – Information about each block of text in the input document. Blocks can nest within a block. A page block contains a block for each line of text, which contains a block for each word. This field is present in the response for a request that included the Byte parameter.

  • BlockReferences – A reference to each block for this entity. This field is present in the response for a request that included the Byte parameter. The field isn't present for text files.

  • Errors – Page-level errors that the system detected while processing the input document. The field is empty if the system encountered no errors.

For more details about these output fields, see DetectEntities in the Amazon Comprehend API Reference

The following example shows the output for a one-page native PDF input document.

Example output from a custom entity recognition analysis of a PDF document
{ "Blocks": [ { "BlockType": "LINE", "Geometry": { "BoundingBox": { "Height": 0.012575757575757575, "Left": 0.0, "Top": 0.0015063131313131314, "Width": 0.02262091503267974 }, "Polygon": [ { "X": 0.0, "Y": 0.0015063131313131314 }, { "X": 0.02262091503267974, "Y": 0.0015063131313131314 }, { "X": 0.02262091503267974, "Y": 0.014082070707070706 }, { "X": 0.0, "Y": 0.014082070707070706 } ] }, "Id": "4330efed-6334-4fc4-ba48-e050afa95c8d", "Page": 1, "Relationships": [ { "ids": [ "f343ce48-583d-4abe-b84b-a232e266450f" ], "type": "CHILD" } ], "Text": "S-3" }, { "BlockType": "WORD", "Geometry": { "BoundingBox": { "Height": 0.012575757575757575, "Left": 0.0, "Top": 0.0015063131313131314, "Width": 0.02262091503267974 }, "Polygon": [ { "X": 0.0, "Y": 0.0015063131313131314 }, { "X": 0.02262091503267974, "Y": 0.0015063131313131314 }, { "X": 0.02262091503267974, "Y": 0.014082070707070706 }, { "X": 0.0, "Y": 0.014082070707070706 } ] }, "Id": "f343ce48-583d-4abe-b84b-a232e266450f", "Page": 1, "Relationships": [], "Text": "S-3" } ], "DocumentMetadata": { "PageNumber": 1, "Pages": 1 }, "DocumentType": "NativePDF", "Entities": [ { "BlockReferences": [ { "BeginOffset": 25, "BlockId": "4330efed-6334-4fc4-ba48-e050afa95c8d", "ChildBlocks": [ { "BeginOffset": 1, "ChildBlockId": "cbba5534-ac69-4bc4-beef-306c659f70a6", "EndOffset": 6 } ], "EndOffset": 30 } ], "Score": 0.9998825926329088, "Text": "0.001", "Type": "OFFERING_PRICE" }, { "BlockReferences": [ { "BeginOffset": 41, "BlockId": "f343ce48-583d-4abe-b84b-a232e266450f", "ChildBlocks": [ { "BeginOffset": 0, "ChildBlockId": "292a2e26-21f0-401b-a2bf-03aa4c47f787", "EndOffset": 9 } ], "EndOffset": 50 } ], "Score": 0.9809727537330395, "Text": "6,097,560", "Type": "OFFERED_SHARES" } ], "File": "example.pdf", "Version": "2021-04-30" }
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