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Clinical text mining using the Amazon Comprehend Medical new SNOMED CT API

Mining medical concepts from written clinical text, such as patient encounters, plays an important role in clinical analytics and decision-making applications, such as population analytics for providers, pre-authorization for payers, and adverse-event detection for pharma companies. Medical concepts contain medical conditions, medications, procedures, and other clinical events. Extracting medical concepts is a complicated process due…



Mining medical concepts from written clinical text, such as patient encounters, plays an important role in clinical analytics and decision-making applications, such as population analytics for providers, pre-authorization for payers, and adverse-event detection for pharma companies. Medical concepts contain medical conditions, medications, procedures, and other clinical events. Extracting medical concepts is a complicated process due to the specialist knowledge required and the broad use of synonyms in the medical field. Furthermore, to make detected concepts useful for large-scale analytics and decision-making applications, they have to be codified. This is a process where a specialist looks up matching codes from a medical ontology, often containing tens to hundreds of thousands of concepts.

To solve these problems, Amazon Comprehend Medical provides a fast and accurate way to automatically extract medical concepts from the written text found in clinical documents. You can now also use a new feature to automatically standardize and link detected concepts to the SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms) ontology. SNOMED CT provides a comprehensive clinical healthcare terminology and accompanying clinical hierarchy, and is used to encode medical conditions, procedures, and other medical concepts to enable big data applications.

This post details how to use the new SNOMED CT API to link SNOMED CT codes to medical concepts (or entities) in natural written text that can then be used to accelerate research and clinical application building. After reading this post, you will be able to detect and extract medical terms from unstructured clinical text, map them to the SNOMED CT ontology (US edition), retrieve and manipulate information from a clinical database, including electronic health record (EHR) systems, and map SNOMED CT concepts to other ontologies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) if your EHR system uses an ontology other than SNOMED CT.

Solution overview

Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning (ML) to extract clinical data from unstructured medical text—no ML experience required—and automatically map them to SNOMED CT, ICD10, or RxNorm ontologies with a simple API call. You can then add the ontology codes to your EHR database to augment patient data or link to other ontologies as desired through OMOP CDM. For this post, we demonstrate the solution workflow as shown in the following diagram with code based on the example sentence “Patient X was diagnosed with insomnia.”

To use clinical concept codes based on a text input, we detect and extract clinical terms, connect to the clinical data base, transform SNOMED code to OMOP CDM code, and use them within our records.

For this post, we use the OMOP CDM as a database schema as an example. Historically, healthcare institutions in different regions and countries use their own terminologies and classifications for their own purposes, which prevents the interoperability of the systems. While SNOMED CT standardizes medical concepts with a clinical hierarchy, the OMOP CDM provides a standardization mechanism to move from one ontology to another, with an accompanying data model. The OMOP CDM standardizes the format and content of observational data so that standardized applications, tools and methods can be applied across different datasets. In addition, the OMOP CDM makes it easier to convert codes from one vocabulary to another by having maps between medical concepts in different hierarchical ontologies and vocabularies. The ontologies hierarchy is set such that descendants are more specific than ascendants. For example, non-small cell lung cancer is a descendent of malignant neoplastic disease. This allows querying and retrieving concepts and all their hierarchical descendants, and also enables interoperability between ontologies.

We demonstrate implementing this solution with the following steps:

  1. Extract concepts with Amazon Comprehend Medical SNOMED CT and link them to the SNOMED CT (US edition) ontology.
  2. Connecting to the OMOP CDM.
  3. Map the SNOMED CT code to OMOP CDM concept IDs.
  4. Use the structured information to perform the following actions:
    1. Retrieve the number of patients with the disease.
    2. Traverse the ontology.
    3. Map to other ontologies.


Before you get started, make sure you have the following:

  • Access to an AWS account.
  • Permissions to create an AWS CloudFormation.
  • Permissions to call Amazon Comprehend Medical from Amazon SageMaker.
  • Permissions to query Amazon Redshift from SageMaker.
  • The SNOMED CT license. SNOMED International is a strong member-owned and driven organization with free use of SNOMED CT within the member’s territory. Members manage the release, distribution, and sub-licensing of SNOMED CT and other products of the association within their territory.

This post assumes that you have an OMOP CDM database set up in Amazon Redshift. See Create data science environments on AWS for health analysis using OHDSI to set up a sample OMOP CDM in your AWS account using CloudFormation templates.

Extract concepts with Amazon Comprehend Medical SNOMED CT

You can extract SNOMED CT codes using Amazon Comprehend Medical with two lines of code. Assume you have a document, paragraph, or sentence:

clinical_note = “Patient X was diagnosed with insomnia.”

First, we instantiate the Amazon Comprehend Medical client in boto3. Then, we simply call Amazon Comprehend Medical’s SNOMED CT API:

import boto3 cm_client = boto3.client(“comprehendmedical”) response = cm_client.infer-snomedct(Text=clinical_note)

Done! In our example, the response is as follows:

{‘Characters’: {‘OriginalTextCharacters’: 38}, ‘Entities’: [{‘Attributes’: [], ‘BeginOffset’: 29, ‘Category’: ‘MEDICAL_CONDITION’, ‘EndOffset’: 37, ‘Id’: 0, ‘SNOMEDCTConcepts’: [{‘Code’: ‘193462001’, ‘Description’: ‘Insomnia (disorder)’, ‘Score’: 0.7997841238975525}, {‘Code’: ‘191997003’, ‘Description’: ‘Persistent insomnia ‘ ‘(disorder)’, ‘Score’: 0.6464713215827942}, {‘Code’: ‘762348004’, ‘Description’: ‘Acute insomnia (disorder)’, ‘Score’: 0.6253700256347656}, {‘Code’: ‘59050008’, ‘Description’: ‘Initial insomnia ‘ ‘(disorder)’, ‘Score’: 0.6112624406814575}, {‘Code’: ‘24121004’, ‘Description’: ‘Insomnia disorder related ‘ ‘to another mental ‘ ‘disorder (disorder)’, ‘Score’: 0.6014388203620911}], ‘Score’: 0.9989109039306641, ‘Text’: ‘insomnia’, ‘Traits’: [{‘Name’: ‘DIAGNOSIS’, ‘Score’: 0.7624053359031677}], ‘Type’: ‘DX_NAME’}], ‘ModelVersion’: ‘0.0.1’, ‘ResponseMetadata’: {‘HTTPHeaders’: {‘content-length’: ‘873’, ‘content-type’: ‘application/x-amz-json-1.1’, ‘date’: ‘Mon, 20 Sep 2021 18:32:04 GMT’, ‘x-amzn-requestid’: ‘e9188a79-3884-4d3e-b73e-4f63ed831b0b’}, ‘HTTPStatusCode’: 200, ‘RequestId’: ‘e9188a79-3884-4d3e-b73e-4f63ed831b0b’, ‘RetryAttempts’: 0}, ‘SNOMEDCTDetails’: {‘Edition’: ‘US’, ‘Language’: ‘en’, ‘VersionDate’: ‘20200901’}}

The response contains the following:

  • Characters – Total number of characters. In this case, we have 38 characters.
  • Entities – List of detected medical concepts, or entities, from Amazon Comprehend Medical. The main elements in each entity are:
    • Text – Original text from the input data.
    • BeginOffset and EndOffset –The beginning and ending location of the text in the input note, respectively.
    • Category – Category of the detected entity. For example, MEDICAL_CONDITION for medical condition.
    • SNOMEDCTConcepts – Top five predicted SNOMED CT concept codes with the model’s confidence scores (in descending order). Each linked concept code has the following:
      • Code – SNOMED CT concept code.
      • Description – SNOMED CT concept description.
      • Score – Confidence score of the linked SNOMED CT concept.
    • ModelVersion – Version of the model used for the inference.
    • ResponseMetadata – API call metadata.
    • SNOMEDCTDetails – Edition, language, and date of the SNOMED CT version used.

For more information, refer to the Amazon Comprehend Medical Developer Guide. By default, the API links detected entities to the SNOMED CT US edition. To request support for your edition, for example the UK edition, contact us via AWS Support or the Amazon Comprehend Medical forum.

In our example, Amazon Comprehend Medical identifies “insomnia” as a clinical term and provides five ordered SNOMED CT concepts and code that we might be referring to in the sentence. In this example, Amazon Comprehend Medical correctly identifies the clinical term as the most likely option. Therefore, the next step is to extract the response. See the following code:

#Get top predicted SNOMED CT Concept pred_snomed = response[‘Entities’][0][‘SNOMEDCTConcepts’][0]

The content of pred_snomed is as follows, with its predicted SNOMED concept code, concept description, and prediction score (probability):

{ ‘Description’: ‘Insomnia (disorder)’, ‘Code’: ‘193462001’, ‘Score’: 0.803254246711731 }

We have identified clinical terms in our text and linked them to SNOMED CT concepts. We can now use SNOMED CT’s hierarchical structure and relations to other ontologies to accelerate clinical analytics and decision-making application development.

Before we access the database, let’s define some utility functions that are helpful in our operations. First, we must import the necessary Python packages:

import pandas import psycopg2

The following code is a function to connect to the Amazon Redshift database:

def connect_to_db(redshift_parameters, user, password): “””Connect to database and returns connection Args: redshift_parameters (dict): Redshift connection parameters. user (str): Redshift user required to connect. password (str): Password associated to the user Returns: Connection: boto3 redshift connection “”” try: conn = psycopg2.connect( host=redshift_parameters[“url”], port=redshift_parameters[“port”], user=user, password=password, database=redshift_parameters[“database”], ) return conn except psycopg2.Error: raise ValueError(“Failed to open database connection.”)

The following code is a function to run a given query on the Amazon Redshift database:

def execute_query(cursor, query, limit=None): “””Execute query Args: cursor (boto3 cursor): boto3 object pointing and with established connection to Redshift. query (str): SQL query. limit (int): Limit of rows returned by the data frame. Default to ‘None’ for no limit Returns: pd.DataFrame: Data Frame with the query results. “”” try: cursor.execute(query) except: return None columns = [ for c in cursor.description] results = cursor.fetchall() if limit: results = results[:limit] out = pd.DataFrame(results, columns=columns) return out

In the next sections, we connect to the database and run our queries.

Connect to the OMOP CDM

EHRs are often stored in databases using a specific ontology. In our case, we use the OMOP CDM, which contains a large number of ontologies (SNOMED, ICD10, RxNorm, and more), but you can extend the solution to other data models by modifying the queries. The first step is to connect to Amazon Redshift where the EHR data is stored.

Let’s define the variables used to connect the database. You must substitute the placeholder values in the following code within with your actual values based on your Amazon Redshift database:

#Connect to Amazon Redshift Database REDSHIFT_PARAMS = { “url”: ““, “port”: ““, “database”: ““, } REDSHIFT_USER = “” REDSHIFT_PASSWORD = “” conn = connect_to_db(REDSHIFT_PARAMS, REDSHIFT_USER, REDSHIFT_PASSWORD) cursor = conn.cursor()

Map the SNOMED CT code to OMOP CDM concept IDs

The OMOP CDM uses its own concept IDs as data model identifiers across ontologies. Those differ from specific ontology codes such as SNOMED CT’s codes, but you can retrieve them from SNOMED CT codes using pre-built OMOP CDM maps. To retrieve the concept_id of SNOMED CT code 193462001, we use the following query:

query1 = f” SELECT DISTINCT concept_id FROM cmsdesynpuf23m.concept WHERE vocabulary_id=’SNOMED’ AND concept_code='{pred_snomed[‘Code’]}’; ” out_df = execute_query(cursor, query1) concept_id = out_df[‘concept_id’][0] print(concept_id)

The output OMOP CDM concept_id is 436962. The concept ID uniquely identifies a given medical concept in the OMOP CDM database and is used as a primary key in the concept table. This enables linking of each code with patient information in other tables.

Use the structured information map from the SNOMED CT code to OMOP CDM concept ID

Now that we have OMOP’s concept_id, we can run many queries from the database. When we find the particular concept, we can use it for different use cases. For example, we can use it to query population statistics with a given condition, traverse ontologies to bridge operability gaps, and extract the unique hierarchical structure of concepts to achieve the right queries. In this section, we walk you through a few examples.

Retrieve the number of patients with a disease

The first example is retrieving the total number of patients with the insomnia condition that we linked to its appropriate ontology concept using Amazon Comprehend Medical. The following code formulates and runs the corresponding SQL query:

query2 = f” SELECT COUNT(DISTINCT person_id) FROM cmsdesynpuf23m.condition_occurrence WHERE condition_concept_id='{concept_id}’; ” out_df = execute_query(cursor, query2) print(out_df)

In our sample records described in the prerequisites section, the total number of patients in the database that have been diagnosed with insomnia are 26,528.

Traverse the ontology

One of the advantages of using SNOMED CT is that we can exploit its hierarchical taxonomy. Let’s illustrate how via some examples.

Ancestors: Going up the hierarchy

First, let’s find the immediate ancestors and descendants of the concept insomnia. We use concept_ancestor and concept tables to get the parent (ancestor) and children (descendants) of the given concept code. The following code is the SQL statement to output the parent information:

query3 = f” SELECT DISTINCT concept_code, concept_name FROM cmsdesynpuf23m.concept WHERE concept_id IN (SELECT ancestor_concept_id FROM cmsdesynpuf23m.concept_ancestor WHERE descendant_concept_id='{concept_id}’ AND max_levels_of_separation=1); ” out_df = execute_query(cursor, query3) print(out_df)

In the preceding example, we used max_levels_of_separation=1 to limit concept codes that are immediate ancestors. You can increase the number to get more in the hierarchy. The following table summarizes our results.

concept_code concept_name
44186003 Dyssomnia
194437008 Disorders of initiating and maintaining sleep

SNOMED CT offers a polyhierarchical classification, which means a concept can have more than one parent. This hierarchy is also called a directed acyclic graph (DAG).

Descendants: Going down the hierarchy

We can use a similar logic to retrieve the children of the code insomnia:

query4 = f”SELECT DISTINCT concept_code, concept_name FROM cmsdesynpuf23m.concept WHERE concept_id IN (SELECT descendant_concept_id FROM cmsdesynpuf23m.concept_ancestor WHERE ancestor_concept_id='{concept_id}’ AND max_levels_of_separation=1); ” out_df = execute_query(cursor, query4) print(out_df)

As a result, we get 26 descendant codes; the following table shows the first 10 rows.

concept_code concept_name
24121004 Insomnia disorder related to another mental disorder
191997003 Persistent insomnia
198437004 Menopausal sleeplessness
88982005 Rebound insomnia
90361000119105 Behavioral insomnia of childhood
41975002 Insomnia with sleep apnea
268652009 Transient insomnia
81608000 Insomnia disorder related to known organic factor
162204000 Late insomnia
248256006 Not getting enough sleep

We can then use these codes to query a broader set of patients (parent concept) or a more specific one (child concept).

Finding the concept in the appropriate hierarchy level is important, because if not accounted for appropriately, you might get wrong statistical answers from your queries. For example, in the preceding use case, let’s say that you want to find the number of patients with insomnia that is only related with not getting enough sleep. Using the parent concept for the general insomnia gives you a different answer than when specifying the descendant concept code only related with not getting enough sleep.

Map to other ontologies

We can also map the SNOMED concept code to other ontologies such as ICD10CM for conditions and RxNorm for medications. Because insomnia is condition, let’s find the corresponding ICD10 concept codes for the given insomnia’s SNOMED concept code. The following code is the SQL statement and function to find the ICD10 concept codes:

query5 = f” SELECT DISTINCT concept_code, concept_name, vocabulary_id FROM cmsdesynpuf23m.concept WHERE vocabulary_id=’ICD10CM’ AND concept_id IN (SELECT concept_id_2 FROM cmsdesynpuf23m.concept_relationship WHERE concept_id_1='{concept_id}’ AND relationship_id=’Mapped from’); ” out_df = execute_query(cursor, query5) print(out_df)

The following table lists the corresponding ICD10 concept codes with their descriptions.

concept_code concept_name vocabulary_id
G47.0 Insomnia ICD10CM
G47.00 Insomnia, unspecified ICD10CM
G47.09 Other insomnia ICD10CM

When we’re done running SQL queries, let’s close the connection to the database:


Now that you have reviewed this example, you’re ready to apply Amazon Comprehend Medical on your clinical text to extract and link SNOMED CT concepts. We also provided concrete examples of how to use this information with your medical records using an OMOP CDM database to run SQL queries and get patient information related with the medical concepts. Finally, we also showed how to extract the different hierarchies of medical concepts and convert SNOMED CT concepts to other standardized vocabularies such as ICD10CM.

The Amazon ML Solutions Lab pairs your team with ML experts to help you identify and implement your organization’s highest value ML opportunities. If you’d like help accelerating your use of ML in your products and processes, please contact the Amazon ML Solutions Lab.

About the Author

Tesfagabir Meharizghi is a Data Scientist at the Amazon ML Solutions Lab where he helps customers across different industries accelerate their use of machine learning and AWS Cloud services to solve their business challenges.

Miguel Romero Calvo is an Applied Scientist at the Amazon ML Solutions Lab where he partners with AWS internal teams and strategic customers to accelerate their business through ML and cloud adoption.

Lin Lee Cheong is a Senior Scientist and Manager with the Amazon ML Solutions Lab team at Amazon Web Services. She works with strategic AWS customers to explore and apply artificial intelligence and machine learning to discover new insights and solve complex problems.


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Customize pronunciation using lexicons in Amazon Polly

Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington…




Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington Post to allow readers to listen to news articles.

As of today, Amazon Polly provides over 60 voices in 30+ language variants. Amazon Polly also uses context to pronounce certain words differently based upon the verb tense and other contextual information. For example, “read” in “I read a book” (present tense) and “I will read a book” (future tense) is pronounced differently.

However, in some situations you may want to customize the way Amazon Polly pronounces a word. For example, you may need to match the pronunciation with local dialect or vernacular. Names of things (e.g., Tomato can be pronounced as tom-ah-to or tom-ay-to), people, streets, or places are often pronounced in many different ways.

In this post, we demonstrate how you can leverage lexicons for creating custom pronunciations. You can apply lexicons for use cases such as publishing, education, or call centers.

Customize pronunciation using SSML tag

Let’s say you stream a popular podcast from Australia and you use the Amazon Polly Australian English (Olivia) voice to convert your script into human-like speech. In one of your scripts, you want to use words that are unknown to Amazon Polly voice. For example, you want to send Mātariki (Māori New Year) greetings to your New Zealand listeners. For such scenarios, Amazon Polly supports phonetic pronunciation, which you can use to achieve a pronunciation that is close to the correct pronunciation in the foreign language.

You can use the Speech Synthesis Markup Language (SSML) tag to suggest a phonetic pronunciation in the ph attribute. Let me show you how you can use SSML tag.

First, login into your AWS console and search for Amazon Polly in the search bar at the top. Select Amazon Polly and then choose Try Polly button.

In the Amazon Polly console, select Australian English from the language dropdown and enter following text in the Input text box and then click on Listen to test the pronunciation.

I’m wishing you all a very Happy Mātariki.

Sample speech without applying phonetic pronunciation:

If you hear the sample speech above, you can notice that the pronunciation of Mātariki – a word which is not part of Australian English – isn’t quite spot-on. Now, let’s look at how in such scenarios we can use phonetic pronunciation using SSML tag to customize the speech produced by Amazon Polly.

To use SSML tags, turn ON the SSML option in Amazon Polly console. Then copy and paste following SSML script containing phonetic pronunciation for Mātariki specified inside the ph attribute of the tag.

I’m wishing you all a very Happy Mātariki.

With the tag, Amazon Polly uses the pronunciation specified by the ph attribute instead of the standard pronunciation associated by default with the language used by the selected voice.

Sample speech after applying phonetic pronunciation:

If you hear the sample sound, you’ll notice that we opted for a different pronunciation for some of vowels (e.g., ā) to make Amazon Polly synthesize the sounds that are closer to the correct pronunciation. Now you might have a question, how do I generate the phonetic transcription “” for the word Mātariki?

You can create phonetic transcriptions by referring to the Phoneme and Viseme tables for the supported languages. In the example above we have used the phonemes for Australian English.

Amazon Polly offers support in two phonetic alphabets: IPA and X-Sampa. Benefit of X-Sampa is that they are standard ASCII characters, so it is easier to type the phonetic transcription with a normal keyboard. You can use either of IPA or X-Sampa to generate your transcriptions, but make sure to stay consistent with your choice, especially when you use a lexicon file which we’ll cover in the next section.

Each phoneme in the phoneme table represents a speech sound. The bolded letters in the “Example” column of the Phoneme/Viseme table in the Australian English page linked above represent the part of the word the “Phoneme” corresponds to. For example, the phoneme /j/ represents the sound that an Australian English speaker makes when pronouncing the letter “y” in “yes.”

Customize pronunciation using lexicons

Phoneme tags are suitable for one-off situations to customize isolated cases, but these are not scalable. If you process huge volume of text, managed by different editors and reviewers, we recommend using lexicons. Using lexicons, you can achieve consistency in adding custom pronunciations and simultaneously reduce manual effort of inserting phoneme tags into the script.

A good practice is that after you test the custom pronunciation on the Amazon Polly console using the tag, you create a library of customized pronunciations using lexicons. Once lexicons file is uploaded, Amazon Polly will automatically apply phonetic pronunciations specified in the lexicons file and eliminate the need to manually provide a tag.

Create a lexicon file

A lexicon file contains the mapping between words and their phonetic pronunciations. Pronunciation Lexicon Specification (PLS) is a W3C recommendation for specifying interoperable pronunciation information. The following is an example PLS document:

Matariki Mātariki NZ New Zealand

Make sure that you use correct value for the xml:lang field. Use en-AU if you’re uploading the lexicon file to use with the Amazon Polly Australian English voice. For a complete list of supported languages, refer to Languages Supported by Amazon Polly.

To specify a custom pronunciation, you need to add a element which is a container for a lexical entry with one or more element and one or more pronunciation information provided inside element.

The element contains the text describing the orthography of the element. You can use a element to specify the word whose pronunciation you want to customize. You can add multiple elements to specify all word variations, for example with or without macrons. The element is case-sensitive, and during speech synthesis Amazon Polly string matches the words inside your script that you’re converting to speech. If a match is found, it uses the element, which describes how the is pronounced to generate phonetic transcription.

You can also use for commonly used abbreviations. In the preceding example of a lexicon file, NZ is used as an alias for New Zealand. This means that whenever Amazon Polly comes across “NZ” (with matching case) in the body of the text, it’ll read those two letters as “New Zealand”.

For more information on lexicon file format, see Pronunciation Lexicon Specification (PLS) Version 1.0 on the W3C website.

You can save a lexicon file with as a .pls or .xml file before uploading it to Amazon Polly.

Upload and apply the lexicon file

Upload your lexicon file to Amazon Polly using the following instructions:

  1. On the Amazon Polly console, choose Lexicons in the navigation pane.
  2. Choose Upload lexicon.
  3. Enter a name for the lexicon and then choose a lexicon file.
  4. Choose the file to upload.
  5. Choose Upload lexicon.

If a lexicon by the same name (whether a .pls or .xml file) already exists, uploading the lexicon overwrites the existing lexicon.

Now you can apply the lexicon to customize pronunciation.

  1. Choose Text-to-Speech in the navigation pane.
  2. Expand Additional settings.
  3. Turn on Customize pronunciation.
  4. Choose the lexicon on the drop-down menu.

You can also choose Upload lexicon to upload a new lexicon file (or a new version).

It’s a good practice to version control the lexicon file in a source code repository. Keeping the custom pronunciations in a lexicon file ensures that you can consistently refer to phonetic pronunciations for certain words across the organization. Also, keep in mind the pronunciation lexicon limits mentioned on Quotas in Amazon Polly page.

Test the pronunciation after applying the lexicon

Let’s perform quick test using “Wishing all my listeners in NZ, a very Happy Mātariki” as the input text.

We can compare the audio files before and after applying the lexicon.

Before applying the lexicon:

After applying the lexicon:


In this post, we discussed how you can customize pronunciations of commonly used acronyms or words not found in the selected language in Amazon Polly. You can use SSML tag which is great for inserting one-off customizations or testing purposes. We recommend using Lexicon to create a consistent set of pronunciations for frequently used words across your organization. This enables your content writers to spend time on writing instead of the tedious task of adding phonetic pronunciations in the script repetitively. You can try this in your AWS account on the Amazon Polly console.

Summary of resources

About the Authors

Ratan Kumar is a Solutions Architect based out of Auckland, New Zealand. He works with large enterprise customers helping them design and build secure, cost-effective, and reliable internet scale applications using the AWS cloud. He is passionate about technology and likes sharing knowledge through blog posts and twitch sessions.

Maciek Tegi is a Principal Audio Designer and a Product Manager for Polly Brand Voices. He has worked in professional capacity in the tech industry, movies, commercials and game localization. In 2013, he was the first audio engineer hired to the Alexa Text-To- Speech team. Maciek was involved in releasing 12 Alexa TTS voices across different countries, over 20 Polly voices, and 4 Alexa celebrity voices. Maciek is a triathlete, and an avid acoustic guitar player.


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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…




This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia


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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…




A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.


For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!


If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.


In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.


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