ComprehendConnector

Accesses the Amazon AI Comprehend Service for natural language processing on text.

Typical Uses

Submitting text to the Amazon AWS Comprehend service for analysis to:
- Detect dominant language
- Determine sentiment information
- Detect key phrases
- Detect entities

How does it work?

The ComprehendConnector uses your Amazon AWS account credentials (either via a previously defined FME web connection, or by setting up a new FME web connection right from the transformer) to access the natural language processing service.

It will submit text to the service, and return features with attributes about that text. Each input text feature may result in several output features.

Usage Notes

For better performance, requests to the Comprehend service are made in parallel, and are returned as soon as they complete. Consequently, detection results will not be returned in the same order as their associated requests.
All confidence scores are returned between 0 and 1. For more information about accuracy, see the Amazon Comprehend FAQs: https://aws.amazon.com/comprehend/faqs/

Configuration

Input Ports

Output Ports

Output

Output will depend on the analysis chosen.

Language Detection

Detects the dominant language for text. The service may return multiple language guesses for an individual request.

Attributes

_language_code	The language code guessed for the text. A list of available languages is available at: https://docs.aws.amazon.com/comprehend/latest/dg/supported-languages.html
_confidence	The probability that a given prediction is correct.
_text	The text analyzed.

Sentiment Detection

Detects the sentiment for text.

Attributes

_sentiment	The sentiment for the text. Possible values are: POSITIVE NEGATIVE NEUTRAL MIXED
_sentiment_postive	The confidence score for positive sentiment.
_sentiment_negative	The confidence score for negative sentiment.
_sentiment_neutral	The confidence score for neutral sentiment.
_sentiment_mixed	The confidence score for mixed sentiment.
_text	The text analyzed.

Key Phrase Detection

Detects the key phrases in text.

Attributes

_key_phrases{}.text	The key phrases from the text.
_key_phrases{}.confidence	A number between 0 and 1 that indicates the confidence score for the key phrase.
_key_phrases{}.begin_offset	The key phrase beginning offset in the text.
_key_phrases{}.end_offset	The key phrase ending offset in the text.
_text	The text analyzed.

Entity Detection

Detects the entities in text. The service can return multiple entities in a given text.

Attributes

_entities{}.text	The entity from the text.
_entities{}.confidence	A number between 0 and 1 that indicates the confidence score for the entity.
_entities{}.begin_offset	The entity beginning offset in the text.
_entities{}.end_offset	The entity ending offset in the text.
_entities{}.type	The type of the detected entity. Types can be found here: https://docs.aws.amazon.com/comprehend/latest/dg/how-entities.html
_text	The text analyzed.

Parameters

Authentication

Credential Source	The ComprehendConnector can use credentials from different sources. Using a web connection integrates best with FME, but in some cases, you may wish to use one of the other sources. Web Connection - use an Amazon Web Services web connection stored in the FME web connections database Embedded - embed an access key ID and secret access key as parameters in the transformer System - use credentials already configured on the system. This allows for using IAM role-based authentication. For more information about configuring AWS credentials system-wide, see https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html#config-settings-and-precedence
Account	Available when the credential source is Web Connection. To create a Comprehend connection, click the 'Account' drop-down box and select 'Add Web Connection...'. The connection can then be managed via Tools -> FME Options... -> Web Connections.
Region	The AWS Region through which to access Comprehend. To optimize latency, it is best practice to specify the correct region.
Access Key and Secret Access Key	Available when the credential source is Embedded. An access key ID and secret access key can be specified directly in the transformer instead of in a web connection.

Request

Text

The text to be analyzed under the specified request action.

Action

The type of operation to perform. Choices are:

Language Detection	Detects language(s) for the text.
Key Phrase Detection	Detects key phrase(s) in the text.
Entity Detection	Detects entities in the text.
Sentiment Detection	Detects sentiments in the text.

Editing Transformer Parameters

Transformer parameters can be set by directly entering values, using expressions, or referencing other elements in the workspace such as attribute values or user parameters. Various editors and context menus are available to assist. To see what is available, click beside the applicable parameter.

How to Set Parameter Values

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Content Types

String Functions	These functions manipulate and format strings.
Special Characters	A set of control characters is available in the Text Editor.
Math Functions	Math functions are available in both editors.
Date/Time Functions	Date and time functions are available in the Text Editor.
Math Operators	These operators are available in the Arithmetic Editor.
FME Feature Functions	These return primarily feature-specific values.
FME Parameters	FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters	Create your own editable parameters.

Dialog Options - Tables

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering

Enabled once you have clicked on a row item. Choices include:

Add a row
Remove a row
Move current row up one
Move current row down one
Move current row to top
Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

Cut a row - delete and copy to clipboard
Copy a row to the clipboard
Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

For more information, see Transformer Parameter Menu Options.

Reference

Processing Behavior	Feature-Based
Feature Holding	No
Dependencies	Amazon AWS Account with Comprehend access
Aliases	AmazonAWSComprehendConnector
History	Released FME 2019.2

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Examples may contain information licensed under the Open Government Licence – Vancouver, Open Government Licence - British Columbia, and/or Open Government Licence – Canada.