- [Instructor] Ever wonder how machines understand
and interpret human language?
Next, we will explore how you can leverage
Azure AI Natural Language Processing Services
to build smart applications.
This is crucial for AI-102 certification exam covering
about 30 to 35% of the content.
With just three services,
you can master a significant portion of the exam;
that's quite low-hanging fruit.
Let's dive in and see
how Azure AI can help you excel in NLP.
So what are we focusing on in this section?
It's all about integrating
and managing NLP services in Azure to build applications
that can understand and interpret human language.
Here is a high-level overview of what we will cover,
what is covered on exam.
One is analyze text by using Azure AI Language.
Second is process speech by using AI speech,
and then we have translate language and implement
and manage a language understanding model.
And last but not least is create
a question answering solution.
Let's break this down a bit.
We have analyze text by using Azure AI Language.
There we can extract key phrases
and entities to pull out important terms
and entities from text.
We have determined sentiment
to figure out if the text is positive, negative, or neutral.
We have detect language for identifying
what language the text is written in,
or identify PII to detect
and handle personally identifiable information.
We have as well the other category is process speech
by using Azure AI Speech, which involves text-to-speech
and speech-to-text to convert written text
to spoken words and vice versa.
Next, we have enhance with SSML.
This is using speech synthesis markup language
to improve text-to-speech.
After this comes custom speech solutions
to tailor speech processing to your needs.
You have any concrete needs, then you can do that with that,
like for example, create a voice that matches yours
or somebody you know.
Then recognize intents and keywords
for understanding what the speaker intends
and highlight key terms.
Other area is translate language.
Here we have real-time translation to translate text
and documents instantly,
and custom translation models
for creating models specific to your domain.
You, for example, are working on concrete area of finance.
You can train a model for translation concretely for this,
so it will never mistake one word for another.
Then we have speech-to-speech
and speech-to-text translation.
This is basically handle translation
across multiple languages simultaneously.
Also, we have implement
and manage a language understanding model,
which involves training, evaluating, deploying,
and testing models in order to develop models
to understand language, and create intents and entities;
this is to set up what the models should recognize,
and optimize models to continuously improve
and fine tune these models.
We have as well this area
which is create a question answering solution
where we can build a knowledge base to collect
and organize information for answering questions.
We can train and test your knowledge base
to ensure it provides accurate answers
and create a multi-turn conversation in order
to handle more complex interactive Q and A.
Last but not least, we have manage a multi-language solution
to support questions and answers in multiple languages.
The services NLP covers from our Azure AI services
are Azure AI Language, Azure AI Speech, also name AI Speech,
and AI Translator.
These foundational services are essential tools
for building cohesive NLP solutions,
and we'll cover in more detail in chapter one.
Please refer to these chapter videos
to reference them anytime.