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Announcing the launch of Amazon Comprehend custom entity recognition real-time endpoints

Amazon Comprehend is a natural language processing (NLP) service that can extract key phrases, places, names, organizations, events, sentiment from unstructured text, and more (for more information, see Detect Entities). But what if you want to add entity types unique to your business, like proprietary part codes or industry-specific terms? In November 2018, Amazon Comprehend […]

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Amazon Comprehend is a natural language processing (NLP) service that can extract key phrases, places, names, organizations, events, sentiment from unstructured text, and more (for more information, see Detect Entities). But what if you want to add entity types unique to your business, like proprietary part codes or industry-specific terms? In November 2018, Amazon Comprehend added the ability to extend the default entity types to detect custom entities.

Until now, inference with a custom entity recognition model was an asynchronous operation.

In this post, we cover how to build an Amazon Comprehend custom entity recognition model and set up an Amazon Comprehend Custom Entity Recognition real time endpoint for synchronous inference. The following diagram illustrates this architecture.

Solution overview

Amazon Comprehend Custom helps you meet your specific needs without requiring machine learning (ML) knowledge. Amazon Comprehend Custom uses automatic ML (AutoML) to build customized NLP models on your behalf, using data you already have.

For example, if you’re looking at chat messages or IT tickets, you might want to know if they’re related to an AWS offering. You need to build a custom entity recognizer that can identify a word or a group of words as a SERVICE or VERSION entity from the input messages.

In this post, we walk you through the following steps to implement a solution for this use case:

  1. Create a custom entity recognizer trained on annotated labels to identify custom entities such as SERVICE or VERSION.
  2. Create a real-time analysis Amazon Comprehend custom entity recognizer endpoint to identify the chat messages to detect a SERVICE or VERSION entity.
  3. Calculate the inference capacity and pricing for your endpoint.

We provide a sample dataset aws-service-offerings.txt. The following screenshot shows example entries from the dataset.

You can provide labels for training a custom entity recognizer in two different ways: entity lists and annotations. We recommend annotations over entity lists because the increased context of the annotations can often improve your metrics. For more information, see Improving Custom Entity Recognizer Performance. We preprocessed the input dataset to generate training data and annotations required for training the custom entity recognizer.

You can download these files below:

After you download these files, upload them to an Amazon Simple Storage Service (Amazon S3) bucket in your account for reference during training. For more information about uploading files, see How do I upload files and folders to an S3 bucket?
For more information about creating annotations or labels for your custom dataset, see Developing NER models with Amazon SageMaker Ground Truth and Amazon Comprehend.

Creating a custom entity recognizer

To create your recognizer, complete the following steps:

  1. On the Amazon Comprehend console, create a custom entity recognizer.
  2. Choose Train recognizer.
  3. For Recognizer name, enter aws-offering-recognizer.
  4. For Custom entity type, enter SERVICE.
  5. Choose Add type.
  6. Enter a second Custom entity type called VERSION.
  7. For Training type, select Using annotations and training docs.
  8. For Annotations location on S3, enter the path for annotations.csv in your S3 bucket.
  9. For Training documents location on S3, enter the path for train.csv in your S3 bucket.
  10. For IAM role, select Create an IAM role.
  11. For Permissions to access, choose Input and output (if specified) S3 bucket.
  12. For Name suffix, enter ComprehendCustomEntity.
  13. Choose Train.

For our dataset, training should take approximately 10 minutes.

When the recognizer training is complete, you can review the training metrics in the Recognizer details section.

Scroll down to see the individual training performance.

For more information about understanding these metrics and improving recognizer performance, see Custom Entity Recognizer Metrics.

When training is complete, you can use the recognizer to detect custom entities in your documents. You can quickly analyze single documents up to 5 KB in real time, or analyze a large set of documents with an asynchronous job (using Amazon Comprehend batch processing).

Creating a custom entity endpoint

Creating your endpoint is a two-step process: building an endpoint and then using it by running a real-time analysis.

Building the endpoint

To create your endpoint, complete the following steps:

  1. On the Amazon Comprehend console, choose Customization.
  2. Choose Custom entity recognition.
  3. From the Recognizers list, choose the name of the custom model for which you want to create the endpoint and follow the link. The endpoints list on the custom model details page is displayed. You can also see previously created endpoints and the models they’re associated with.
  4. Select your model.
  5. From the Actions drop-down menu, choose Create endpoint.
  6. For Endpoint name, enter DetectEntityServiceOrVersion.

The name must be unique within the AWS Region and account. Endpoint names have to be unique even across recognizers.

  1. For Inference units, enter the number of inference units (IUs) to assign to the endpoint.

We discuss how to determine how many IUs you need later in this post.

  1. As an optional step, under Tags, enter a key-value pair as a tag.
  2. Choose Create endpoint.

The Endpoints list is displayed, with the new endpoint showing as Creating. When it shows as Ready, you can use the endpoint for real-time analysis.

Running real-time analysis

After you create the endpoint, you can run real-time analysis using your custom model.

  1. For Analysis type, select Custom.
  2. For Endpoint, choose the endpoint you created.
  3. For Input text, enter the following:
    AWS Deep Learning AMI (Amazon Linux 2) Version 220 The AWS Deep Learning AMIs are prebuilt with CUDA 8 and several deep learning frameworks.The DLAMI uses the Anaconda Platform with both Python2 and Python3 to easily switch between frameworks.
    

  4. Choose Analyze.

You get insights as in the following screenshot, with entities recognized as either SERVICE or VERSION and their confidence score.

You can experiment with different input text combinations to compare and contrast the results.

Determining the number of IUs you need

The number of IUs you need depends on the number of characters you send in your request and the throughput you need from Amazon Comprehend. In this section, we discuss two different use cases with different costs.

In all cases, endpoints are billed in 1-second increments, with a minimum of 60 seconds. Charges continue to incur from the time you provision your endpoint until it’s deleted, even if no documents are analyzed. For more information, see Amazon Comprehend Pricing.

Use case 1

In this use case, you receive 10 messages/feeds every minute, and each message is comprised of 360 characters that you need to recognize entities for. This equates to the following:

  • 60 characters per second (360 characters x 10 messages ÷ 60 seconds)
  • An endpoint with 1 IU provides a throughput of 100 characters per second

You need to provision an endpoint with 1 IU. Your recognition model has the following pricing details:

  • The price for 1 IU is $0.0005 per second
  • You incur costs from the time you provision your endpoint until it’s deleted, regardless of how many inference calls are made
  • If you’re running your real-time endpoint for 12 hours a day, this equates to a total cost of $21.60 ($0.0005 x 3,600 seconds x 12 hours) for inference
  • The model training and model management costs are the same as for asynchronous entity recognition at $3.00 and $0.50, respectively

The total cost of an hour of model training, a month of model management, and inference using a real-time entity recognition endpoint for 12 hours a day is $25.10 per day.

Use case 2

In this second use case, your requirement increased to run inference for 50 messages/feeds every minute, and each message contains 600 characters that you need to recognize entities for. This equates to the following:

  • 500 characters per second (600 characters x 50 messages ÷ 60 seconds)
  • An endpoint with 1 IU provides a throughput of 100 characters per second.

You need to provision an endpoint with 5 IU. Your model has the following pricing details:

  • The price for 1 IU the $0.0005 per second
  • You incur costs from the time you provision your endpoint until it’s deleted, regardless of how many inference calls are made
  • If you’re running your real-time endpoint for 12 hours a day, this equates to a total cost of $108 (5 x $0.0005 x 3,600 seconds x 12 hours) for inference
  • The model training and model management costs are the same as for asynchronous entity recognition at $3.00 and $0.50, respectively

The total cost of an hour of model training, a month of model management, and inference using a real-time entity recognition endpoint with a throughput of 5 IUs for 12 hours a day is $111.50.

Cleaning up

To avoid incurring future charges, stop or delete resources (the endpoint, recognizer, and any artifacts in Amazon S3) when not in use.

To delete your endpoint, on the Amazon Comprehend console, choose the entity recognizer you created. In the Endpoints section, choose Delete.

To delete your recognizer, in the Recognizer details section, choose Delete.

For instructions on deleting your S3 bucket, see Deleting or emptying a bucket.

Conclusion

This post demonstrated how easy it is to set up an endpoint for real-time text analysis to detect custom entities that you trained your Amazon Comprehend custom entity recognizer on. Custom entity recognition extends the capability of Amazon Comprehend by enabling you to identify new entity types not supported as one of the preset generic entity types. With Amazon Comprehend custom entity endpoints, you can now easily derive real-time insights on your custom entity detection models, providing a low latency experience for your applications. We’re interested to hear how you would like to apply this new feature to your use cases. Please share your thoughts and questions in the comments section.


About the Authors

Mona Mona is an AI/ML Specialist Solutions Architect based out of Arlington, VA. She works with the World Wide Public Sector team and helps customers adopt machine learning on a large scale. She is passionate about NLP and ML explainability areas in AI/ML.

Prem Ranga is an Enterprise Solutions Architect based out of Houston, Texas. He is part of the Machine Learning Technical Field Community and loves working with customers on their ML and AI journey. Prem is passionate about robotics, is an autonomous vehicles researcher, and also built the Alexa-controlled Beer Pours in Houston and other locations.

Source: https://aws.amazon.com/blogs/machine-learning/announcing-the-launch-of-amazon-comprehend-custom-entity-recognition-real-time-endpoints/

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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How to take S3 backups with DejaDup on Ubuntu 20.10

DejaDup is the default backup application for Gnome. It’s a GUI for duplicity, focuses on simplicity, supports incremental encrypted backups and up until recently supported a large number of cloud providers. Unfortunately as of version 42.0, all major cloud providers have been removed. Thus given that Ubuntu 20.10 ships with the specific version, any user […]

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DejaDup is the default backup application for Gnome. It’s a GUI for duplicity, focuses on simplicity, supports incremental encrypted backups and up until recently supported a large number of cloud providers. Unfortunately as of version 42.0, all major cloud providers have been removed. Thus given that Ubuntu 20.10 ships with the specific version, any user who upgrades and has backups on Amazon S3 won’t be able to access them. In this blog post, we will provide a solution that will allow you to continue taking backups on AWS S3 using DejaDup.

The mandatory rant (feel free to skip)

The removal of the cloud providers should not come as a surprise. I’m not exactly sure which version of DejaDup deprecated them but it was around the release of Ubuntu 17.10 when they were all hidden as an option. So for 3 long years, people who had backups on Amazon S3, Google Cloud Storage, Openstack Swift, Rackspace etc could still use the deprecated feature and prepare for the inevitable removal.

So why complain you might ask? Well, first of all, when you update from an earlier version of Ubuntu to 20.10, you don’t really know that the all cloud providers are removed from DejaDup. Hence if something goes wrong during the update, you won’t be able to easily access your backups and restore your system.

Another big problem is the lack of storage options on the last version of DejaDup. They decided to change their policy and support only “consumer-targeted cloud services” but currently they only support Google Drive. So they eliminated all the cost efficient options for mass storage and kept only one single very expensive option. I’m not really sure how this is good for the users of the application. Linux was always about having a choice (too much of it in many cases), so why not maintain multiple storage options to serve both the experience and inexperienced users? Thankfully because we are on Linux, we have option to fix this.

How to use Deja Dup v42+ with AWS S3

WARNING: I have not tested thoroughly the following setup so use it at your own risk. If the computer explodes in your face, you lose your data, or your spouse takes your kids and leaves you, don’t blame me.

Installing s3fs fuse

With that out of the way, let’s proceed to the fix. We will use s3fs fuse, a program that allows you to mount an S3 bucket via FUSE and effectively make it look like a local disk. Thankfully you don’t have to compile it from source as it’s on Ubuntu’s repos. To install it, type the following in your terminal:

sudo apt install s3fs

Setting up your AWS credentials file

Next, we need to configure your credentials. The s3fs supports two methods for authentication: an AWS credential file or a custom passwd file. In this tutorial we will use the first method but if you are interested for the latter feel free to view the s3fs documentation on Github. To setup your credentials make sure that the file ~/.aws/credentials contains your AWS access id and secret key. It should look like this:


[default]
aws_access_key_id=YOUR_ACCESS_KEY_ID
aws_secret_access_key=YOUR_SECRET_ACCESS_KEY

Mounting your bucket to your local filesystem

Once your have your credentials file you are ready to mount your backup bucket. If you don’t remember the bucket name you can find it by visiting your AWS account. To mount and unmount the bucket to/from a specific location type:


# mount
s3fs BUCKET_NAME /path/to/location

# unmount
fusermount -u /path/to/location

Mounting the bucket like this is only temporary and will not persist across reboots. You can add it on /etc/fstab but I believe this only works with the passwd file. If you want to use your AWS credentials file an easy workaround it to create a shortcut in your Startup Applications Preferences.

Note that you can add a small 10 sec delay to ensure that the WiFi is connected before you try to mount the bucket. Internet access is obviously necessary for mounting it successfully. If you are behind VPNs or have other complex setups, you can also create a bash script that makes the necessary checks before you execute the mount command. Sky is the limit!

Configuring DejaDup

With the bucket mounted as a local drive, we can now easily configure DejaDup to use it. First of all we need to change the backend to local. This can be done either by using a program like dconfig or the console with the following command:

gsettings set org.gnome.DejaDup backend 'local'

Finally we open DejaDup, go to preferences and point the storage location to the directory that has your S3 backup files. Make sure you select the subdirectory that contains the backup files; this is typically a subdirectory in your mount point that has name equal to your computer’s hostname. Last but not least, make sure that the S3 mount directory is excluded from DejaDup! To do this, check the ignored folders in Preferences.

That’s it! Now go to your restore tab and DejaDup will be able to read your previous backups. You can also take new ones.

Gotchas

There are a few things to keep in mind in this setup:

  1. First of all, you must be connected on the internet when you mount the bucket. If you are not the bucket won’t be mounted. So, I advise you instead of just calling the mount command, to write a bash script that does the necessary checks before mounting (internet connection is on, firewall allows external requests etc).
  2. Taking backups like that seems slower than using the old native S3 support and it is likely to generate more network traffic (mind AWS traffic costs!). This is expected because DejaDup thinks it’s accessing the local file-system, so there is no need for aggressive caching or minimization of operations that cause network traffic.
  3. You should expect stability issues. As we said earlier, DejaDup does not know it writes data over the wire so much of the functionalities that usually exist in such setups (such as retry-on-fail) are missing. And obviously if you lose connection midway of the backup you will have to delete it and start a new one to avoid corrupting your future backups.
  4. Finally keep in mind that this is a very experimental setup and if you really want to have a reliable solution, you should do your own research and select something that meets your needs.

If you have a recommendation for an Open-Source Backup solution that allows locally encrypted incremental backups, supports S3 and has an easy to use UI please leave a comment as I’m more than happy to give it a try.

About Vasilis Vryniotis

My name is Vasilis Vryniotis. I’m a Data Scientist, a Software Engineer, author of Datumbox Machine Learning Framework and a proud geek. Learn more

Source: http://blog.datumbox.com/how-to-take-s3-backups-with-dejadup-on-ubuntu-20-10/

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