Natural Language Processing
Natural language refers to human language, such as English, Spanish and Japanese- the same languages we speak on a daily basis. Natural Language Processing (NLP) is the field of computer science and artificial intelligence that is in charge of analyzing all of the natural language information sent by humans to interact with machines.
These are many ways the humans can communicate with machines, from text messages and other written documents to audio records, and much more. However, the process of reading and understanding this messages is far more complex than it may look at first. Take for this phrase, for example:
"Alex was on fire! He tore up the stage"
As you can see, for a human, it's probably quite obvious what this sentence means. We can figure out that Alex may be some kind of artist- probably a musician. When we see "on fire" and "tore up", we know that it means he played really well on the stage.
However, computers tend to take things more literally. A computer would see the word, "Alex" and, based on the capitalization, assume it's a person or place. But then it would read that Alex "was on fire" and assume that Alex is literally on fire and literally destroying a stage. This is a common error that can only be solved with machine learning, which consists of providing the application AI with different examples to compare and understand the natural language. This is called Natural Language Processing.
iOS and Natural Language Processing
With iOS, there are many tools that use natural language to interact with the user, for example Alexa from Amazon, Siri from Apple, or Dialog flow from Google. All of these tools have the same goal- to help computer machines engage in communication with the user using natural human language. If this is done correctly, we can create applications that help automatize services and provide immediate responses to customer problems, inquiries, order request, and more.
When you are creating an NLP application for a device or service, you need to first focus on what kind of problem your application will solve, next what kind of information your application needs to solve that problem and then how the application will interact with the user to get this information.
As an example, if we are going to create a customer service application and one of its function is to give the user our customer service schedule, the user should be able to send text messages that contain information about what they are asking the application, for example:
- At what time you close?
- At what time you open?
- What are the hours of service?
As you can see, all the messages have similar requests, but different structures. The job of NLP is to store and analyze the message, to identify what are the different words or phrases people might say to signal their goal and intent? In this example, the keywords could be Open, Close or Hours of service. The app would then proceed to create an appropriate answer that correctly utilizes the natural language and satisfies the user request.
When we are creating an NLP application, we need to remember that we are talking to a human and natural conversation isn't perfect. If our application can't identify the request, we need to give users the opportunity to correct errors or change their answers. This way, we can improve our application's ability to handle these exceptions.
"It's always better to say: I don't know the answers to that, than to pretend and give a wrong answer".
Why Implement Natural Language Processing in iOs
With companies continuing to rely on the storage and processing of big data, and iPhones and tablets as one of the most common ways to send information, having an NLP application that enables the interpretation and analysis of all this unstructured text is a powerful tool for any company. With so much information stored in unstructured text files, like medical records, project reports, and product inventories, for example the Natural Language Analysis can sift through all this data and provide information on context, user intents, and even sentiment.
Beyond being able to analyze spoken and written text, NLP has now become the engine behind bots in Slack, Skype, and Facebook that you can almost have a complete human conversation with. If you go to Apple's support website and request to speak to customer service, you will be presented with a web bot that will try and point you in the right direction, based on the question you've asked. It helps customers feel understood in real time, without actually needing to speak to a human- saving company resources and user time.
iOS Microsoft Azure
Microsoft Azure is a powerful tool that offers many services, such as SQL databases, application services, and information storage. With these services, you can save resources in both hardware and software for your app. However, the most important question is: "How can this services help your iOS application?"
Mobile back-end services
Azure gives you the option to create code in your development language of choice and deploy it to the Azure app service. With this, you have the option to handle all of the information analysis gathered by the application with fast response, building rich, serverless scenarios.
Cross Platform Mobile Development
By having your application back-end in Azure app services, you have the ability to interact with both iOS and Android applications. Additionally, having your application's logic in only one place gives you the opportunity to design more simple front-end interfaces for both iOS and Android. This way, you will only need to collect information from the user and send it to the back-end so that Azure can analyze and decide what to do with this information based on user intent.
Wherever your data is, Microsoft Azure offers a lot of different choices to handle and store it, helping you work with NLP applications which need to store large amounts of information. Microsoft Azure also gives you the opportunity to store your information in table storage, SQL, and NoSQL databases, giving you multiple options to handle your app's information.
Is Microsoft Azure a viable option to support you NLP App?
The answer to this question depends on what kind of application are you developing. For many NLP applications, Azure offers a large amount of tools to store, analyze, and organize the information obtained from your NPL application in a very simple and efficient way.