Natural Language Processing in iOS Apps – Features, Siri use case, Process

“AI and ML can make humans more productive than we have ever imagined.” This quote from Sundar Pichai is absolutely true.

We have reached the point where we are undoubtedly training machines to perform human-like activities. From Microsoft to Google and Apple, all the tech giants are investing heavily in machine learning models to train computer systems.

Siri is the first full-fledged example of NLP. It inspires entrepreneurs to build an iOS application that can use the potential of Natural Language Processing. But, if you are new to Natural Language Processing in iOS apps, you would want to learn more about it, its features, and the process of integrating it.

This is the article you need to read to understand everything about Natural Language processing in iOS apps covering non-technical to technical details.

Understanding Natural Language Processing

Natural Language Processing or NLP is a branch where computers are trained to interpret text and spoken words the same way as humans do. Powerful and intelligent algorithms referred to as Machine learning support the NLP technology. In 2023, we have ample examples showcasing the potential of “what machine learning can do.” From robots writing articles to coding, Natural Language Processing technology is making so much possible that humans have never imagined.

In simple words, imagine a computer acting completely like humans where you can start a conversation with them, either in voice format or text. It will recognize what you are saying, analyze it, and give you the exact answer that a human would say.

Furthermore, tools like Open AI Codex showcase the potential of NLP where robots can write programming codes for you. The whole technology is known as Natural Language Processing. Chat GPT is a great example of Natural Language Processing Technology.

Features of Natural Language Processing

The major features of Natural Language Processing include the following:

Content categorization involves content alerts, duplication detection, indexing, and more.
Topic Discovery and modeling to identify the theme and meaning of the text.
Content extraction to extract the data from the image sources.
Emotion and sentiment analysis to read and analyze the mood from text or voice.

Brief History

The Concept of natural language processing goes back to the early 1900s. A Swiss linguistic professor named FERDINAND DE Saussure laid the concept of Language as a science. His approach to the concept described language as systems with conditional reasoning. The approach matured over time and came to be known as Structuralism in Language Learning.

In 1966 the funding and investment for researching NLP reached $20 Million dollars making it more expensive than human translations. However, there was no sign of even the basic conversation putting a halt on this technology till 1980.

Additionally, IMDB took charge and built successful statistical models. In 2011, Siri became the world’s first successful NLP/AI assistant. It was equipped with advanced technologies like Speech recognition and Voice Command systems. Furthermore, it was machine learning that contributed to the advancement of Natural Language Processing with the help of big data analysis.

Role of Machine Learning in Natural Language Processing

Machine Learning Algorithms ensure that Natural Language Processing is able to achieve what it aims. Whether it is about text analytics or speech recognition, machine learning algorithms play a key role. The machine learning model changes as the system gathers more learning through the patterns.

While performing the NLP, the machine learning algorithm identifies the parts of speech, entities, sentiments, and other important elements associated with the text. Supervised and Unsupervised learning are two machine learning approaches. And in NLP, supervised machine learning works in language processing and text analytics. Here are the most popular supervised NLP algorithms.

Support Vector Machines
Maximum Entropy
Neural Networks/Deep Learning
Conditional Random Field

Furthermore, unsupervised machine learning techniques like Clustering, Latent Semantic Indexing, and Matrix Factorization make the models mature. You can easily view the use case of Machine learning in Android app development and iOS app development.

Finally, below is the diagram of how Speech Recognition, one of the elements of Natural Language Processing, works.

Speech Recognition Process

Natural Language Processing in iOS Apps- Siri Use Case

Why have we selected iOS to understand the Natural Language Processing use cases? It is because Siri was the first digital voice assistant announced by Apple. And, in a very short period of time, it became one of the most popular matters to study NLP in iOS apps. Tech giants started to research iOS app development agencies to embed Natural Language Processing in their iOS applications.

Siri uses an advanced Text to speech synthesis process normalizing the raw text including numbers, abbreviations, and other components. The goal of Siri’s TTS system is to train a unified model based on machine learning. Additionally, the company has been pushing its algorithms which are visible from iOS 9 to iOS 16. Below is the figure representing Deep Learning for Siri’s Voice by Apple.

deep learning siri voice model

Further, simplifying the working of SIRI here are the steps involved in Siris Natural Language Processing.

Voice recognition
Connecting to Apple’s server
Understanding the meanings of Command
Producing the Results

How to Embed NLP in iOS Applications?

Apple’s Natural Language Framework is something that you must understand while embedding NLP in your iOS application. Additionally, the framework provides the below fundamental building blocks for language processing:

Identification
Tokenization
Part of speech tagging
Lemmatization
Named entity recognition

How to perform Tokenization?

In order to perform tokenization in your iOS application you have to Enumerate the words in a string. You can use NLTokenizer to enumerate the words in natural language text.

let text = """
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"""
let tokenizer = NLTokenizer(unit: .word)
tokenizer.string = text
tokenizer.enumerateTokens(in: text.startIndex..<text.endIndex) { tokenRange, _ in
print(text[tokenRange])
return true
}

How to identify the language in the text?

To identify the language in a text you can use NLLanguageRecognizer. For texts with multiple parts, you can use processString(-:)

// Create a language recognizer.
let recognizer = NLLanguageRecognizer()
recognizer.processString("I want to identify the language.")

It depends upon your goals and what you are trying to achieve. We have showcased to you a few instances prescribed by Apple associated with Natural Language Processing in iOS Applications.

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