We all know that, when typing on our phones, they will provide us with some assistance to avoid making spelling mistakes and predict what we are trying to say. The one responsible for this assistance is the keyboard, and it bases its functionality on machine learning algorithms and deep learning techniques. So, how do predictive text algorithms work?

What is a predictive text?

Let’s first dive into the basic concept to discuss. What exactly is predictive text? When you use the keyboard predictive text in your phone, the system will automatically predict what you are trying to say, and complete it for you. There are different types of prediction: Auto-completion and next-word prediction.

Auto-completion happens when the user types the first letters of a word and the algorithm suggests the rest of it; while next-word prediction tries, as the word says, to suggest the upcoming word(s) in a text.

Nowadays, predictive text is sophisticated and accurate, working with complex algorithms to predict not only the rest of the word but even full phrases. It became popular with the use of smartphones, but, in fact, predictive text was created prior to touchscreens and QWERTY keyboards.

Most common prediction text systems

As said above, predictive text is even older than smartphones. It started off as a quite simple system that later evolved into the technology we currently have in the palm of our hands. Let’s take a look at the evolution of predictive text systems.

T9

The T9 predictive text algorithm was not really predictive, but it was a key predecessor that simplified typing. It was created for 12-key phones. These kinds of phones originally worked with multi-tap typing, which meant that each key represented 3 or 4 letters, and you should press them several times to select the letter you needed. T9 took typing from multi-tap to single-tap; with this system, users only needed to tap each button once, and the algorithm would search for the most probable words that matched that combination of keys in a dictionary and order them in a list.

iTap

iTap was the first system that really focused on text prediction. Also based on dictionaries, iTap worked by analysing the first letters of a word the user typed, looking for coincidences in a dictionary and suggesting the ending of the word based on that. This way, the user should simply tap the word suggested to complete it.

LetterWise/WordWise

Letterwise was a new predictive text system, also for 12-key keypads, which was different from its predecessors because it did not work by searching on a dictionary, but with linguistic components. The database contained in the algorithms does not focus on words but on letter sequencing probabilities. It uses word corpora but not for searching equivalents. Instead, it analysed the probability of appearance of each letter in a context.

How do predictive text algorithms work?

The main difference between old devices and new ones is the time used in typing. Since the creation of T9, keyboards can “understand” what you are trying to say, predict it, and suggest it for you to save time typing. But, how do you use predictive texts to your advantage?

The use of machine learning and deep learning in predictive text algorithms

Predictive text’s “intelligence” doesn’t only consist in suggesting words. One of the most important features of your keyboard predictive text is the use of machine learning and deep learning. This allows the system to consider how often a word is written or accepted by a user and rank it in a list, based on its likelihood of use.

Which AI algorithm helps in the prediction?

Depending on the operative system the prediction engine is set in, text prediction uses different algorithms. As Natural Language Processing (NLP) is so complex, the technologies used for this purpose have to be complex too, and so is deep learning. Deep learning consists in creating neural networks, which are several layers of algorithms that aim at resembling human intelligence. It has several applications, such as facial recognition or image classification, but it is particularly prevalent in NLP.

Deep learning techniques used in NLP are called language models, and they can be classified into recurrent, convolutional and transformer neural networks based on the types of algorithms used in their structures.

Our language model is constantly improving to make text prediction more accurate. Currently, our model is used both by the autocompletion and the next-word prediction features at the same time. This allows the keyboard to make more precise predictions, as it keeps the current word and also the following in mind to make the right suggestions. 

The local dictionary and the predictive keyboard

For mobile keyboards to work this way, developers “feed” them with corpora from different sources, like encyclopaedias and manuals, or social media posts. This way, the system will have a broad dictionary to work with and cover most of the user’s vocabulary. 

Besides, keyboards learn new words that the user writes although they were not in the pre-set database. This way, it creates a local user database. At the same time, the system scores all these words in terms of likelihood of use in order to make more accurate suggestions based on how much users write each word. 

Keyboards don’t only save individual words, but also phrases. This way, they can suggest whole sets of words for users to select and tap instead of type.

Considering these systems use machine learning to adapt and improve, the more you use the keyboard, the more accurate it gets. These keyboards constantly collect information from your typing and retrieve it to create better suggestions. However, if you want to bypass the learning process, most keyboards have the option to input new words manually into the local dictionary or delete suggestions you consider useless.

Fleksy’s text prediction technology

If you are a software developer in the text prediction area, you may be interested in our keyboard solutions. You can find our Fleksy keyboard on Play Store or App store, but the most interesting product we have to offer is our predictive keyboard SDK. By using this software developer kit, you will be able to power your own keyboard interface with fast and accurate text autocorrection as well as prediction capabilities that will ensure your users have shorter typing times and fewer mistakes.

Still, accuracy is not the only great feature our SDK has to offer. One of the most important functions Fleksy-supported keyboards have is the integration of “extensions”. These extensions are “mini apps” embedded within the keyboard to allow users to access other apps’ functions without having to leave the current app they are in. For instance, a user could be writing a WhatsApp message and check the weather forecast just by tapping the weather icon on their keyboard, and even share this information with that user they are talking to.

Text prediction systems vs non-predictive configurations: Advantages and disadvantages

Like with any other technological advancement, some users may be happy while others aren’t. Although we support the use of predictive text and intelligent keyboards, it is true that there are also some disadvantages that can discourage people from using them.

Let’s first take a look at the advantages:

• It’s faster: You can simply tap a suggested word instead of writing it in full. Doesn’t seem like a lot of time saved, but, in accumulation, it is.
• It’s accurate: With text prediction comes autocorrection. These systems being based on dictionaries helps to detect mistakes in writing not only for predicting the correct intended word, but also for correcting what the users wanted to say.
• It has many useful secondary functions: Fleksy’s keyboard not only helps in text writing but also in many other aspects. In terms of digital health, for instance, intelligent keyboards help collect information about user typing habits and register patterns. Incredibly, this can help doctors diagnose patients with mental illnesses or difficulties.

Now let’s see what are the possible disadvantages:

• It might overcorrect you: Intelligent keyboards need to “get used” to the way you write, and depending on which system you are using, it might take a little bit long. During that training period, the predictive text or the autocorrect functions might be quite stubborn and correct words that you are writing properly just because they are not in your local dictionary. The good thing is that the more you use your keyboard, the faster the learning process will take place. And besides, you can always add or delete words from the local dictionary manually.
• It can create a dependency on technology: Considering our phones autocorrect our texts and suggest what to say, we might be too dependent on them for these purposes. We sometimes don’t mind spelling words wrongly or thinking about what word to use, as we let the keyboard do it for us.
• It might seem intrusive: When using intelligent keyboards, it is sometimes scary to see how accurate they are, and we wonder how much information they are collecting from us. However, with Fleksy’s SDK, there’s no need to worry, as all our text processing is happening locally in your smartphone.

Predictive text algorithms and cybersecurity: Are they safe?

As said before, it might be a little scary to see that text prediction is sometimes too accurate, or that it is clearly collecting information from other apps. In the case of other keyboards, this is a real concern, as their data is stored in cloud servers, the information can be accessed by third parties and sold or reused, according to their privacy policies. Instead, our SDK uses local storage and message encryption not only on particular apps but during the global use of our predictive text system. Besides, we constantly focus on making our SDK the safest in the market, so we work on the continuous improvement of privacy-ensuring protocols. 

Can predictive text be hacked?

Depending on the cybersecurity protocols each keyboard uses, this may be a possibility or not. With our SDK, no hacking can take place thanks to data being stored locally in your device, not allowing third parties to use your information, and encrypting messages on any app.

Text prediction on smartphones and computers: Is there a difference?

Predictive texts can be found on almost all electronic devices that require typing, but they are different depending on the device. On smartphones, all applications use the same keyboard, so you are constantly feeding it with information. On computers, each program has its own predictive text system, so each of them will have different inputs depending on how much and what you use them for.

Healthcare, Fintech, Banking, AI and Generative AI, as well as Virtual Reality and Augmented Reality are some of the industries we are helping to create custom keyboards that not only offer one of the most accurate text prediction engines, but also security in terms of privacy and constant additions regarding extensions. 

FAQs

1. What is a prediction algorithm?

Prediction algorithms are used in pattern recognition of different types of data to predict or forecast an upcoming outcome. They are part of the machine learning techniques and they have a number of applications in different areas, such as fintech, digital healthcare and augmented reality. This technology allows companies to make decisions based on historical evidence and also create innovative products for B2B or B2C solutions. 

2. How does text suggestions work?

Text prediction is one of the applications of predictive algorithms. It is based on Natural Language Processing (NLP), which allows software to “understand” the text the user writes on a mobile keyboard and its context, and to predict the following letters or words. The input the user provides when writing is stored and analysed with machine learning techniques for the keyboard to make more accurate suggestions based on commonality of use. 

3. Which algorithm is best for prediction?

There are several types of algorithms that can be used for prediction, but they all have different characteristics and requirements that can make them more or less suitable for specific purposes. For example, prediction can be achieved through linear regression when the data is numerical, decision trees are used in classification, and more complex tasks, such as text prediction, need more complex solutions, like deep learning and neural networks.