In a previous post I discussed some of the most common Natural Language Processing techniques for apps. I referred to NLP back then as the way computers make sense of human language. After all, language can be understood as just another form of data, even if it is a very complex one.
Knowing what are the techniques being used in this dynamic field is important, but it is only part of what matters for app development. Building a great app requires being creative on how features are implemented. How an app integrates existing data processing techniques to deliver new features can be a game-changer for your app.
Language related features for web and mobile apps are one of the things of which we will be hearing a lot in the years to come. In this post I will discuss some of the most important use cases of NLP and how they can be used to develop innovative and engaging features for apps.
Setting Up NLP Features
Before implementing your app’s features, it is important to keep in mind that, like other data-dependent technologies, NLP first needs to be properly set up in order to work correctly. Failing to establish the right parameters of your data management process can compromise your app’s engagement; poor performance of a specific feature might end up affecting your app’s users.
Due to NLP’s strong dependence on data, it is important to make sure that everything related to how it is collected, processed, and stored is aligned with your app’s needs. Having the right data warehouse and cloud services is essential. After all, you want to make sure that your app can handle a considerable amount of users requesting a feature at the same time.
Natural Language Processing Uses and Features
NLP features offer a variety of use cases that can be implemented in an app. These are some of the most common ones that your users will surely love.
This is probably the most common voice feature for apps. Think of this as a virtual assistant that performs a given task at your request. Placing a phone call, sending a message, or playing a song using your voice are all actions that fall under this category.
This can be thought of as a feature that occurs at the intersection of NLP and Affective Computing; this last refers to emotional-based computing. Through a Sentiment Analysis, it is possible to analyze a text and interpret a user’s emotions in order to classify them according to a set of categories. This can be used in retail to understand how a user feels about a product or service but is not limited to it.
Another popular feature is filling the body of a text for users. This can be done in multiple ways. One is through a set of conventional phrases used in everyday language. This applies to formal communications like email and text messages. The other way is through each user’s specific style. In this last, an algorithm can learn to identify a user’s writing style in order to help complete recurrent phrases and words. Word processors and email clients are using this to help users write repetitive messages and other pieces of text easily and faster. Bots can benefit from this, given that they follow some predefined playbooks.
As its name describes, the purpose of this feature is to help users transcribe speech to text. Rather than typing, through the use of NLP voice recognition techniques, algorithms can help make sense of speech, translating it to written text. This is particularly helpful to improve accessibility. Think of people who want to keep track of their notes while on the go or have a disability.
This can be considered as a particular type of request, but because it is so commonly used, it often receives a mention of its own. In this feature, users can ask their voice assistants to look something up for them. Although this is commonly associated with search engines, it can also be integrated within an app. As with the typing feature, it can also help improve accessibility. This is very useful for users who want to browse files using speech rather than a mouse or a keyboard.
The quest for a text generating algorithm with human-like capabilities is getting a lot of attention. If achieved, this has the potential to change how texts are written. In theory, it could help improve human-computer interactions. For the moment, it remains a work in progress.
The world was recently taken by surprise by Open AI’s GPT-3 text generator (GPT stands for Generative Pre-trained Transformer). It is a very powerful AI tool that excels at writing texts that can fool people into believing that they were written by another human. As good as it sounds, GPT-3 has also received critics. Many argue that it is incapable of making sense of information.
Voice translation is like a holy grail for many, and it is totally understandable. If computers were able to translate speech from one language to another, it would open up many possibilities. If this can be done in real-time, it would be even better. As for text generation, this remains a work in progress.
Questions and Answers
One last feature worth mentioning is the possibility to answer users’ questions. This falls, like others on this list, under the category of voice assistants. Ideally, this can be used in apps to help users answer basic questions. This is another area where bots have much to gain.
Experimentation Is Key
There is great potential for implementing these HiTech NLP use cases and features in apps, even if some of them are a long way from working properly. It is important, however, that companies understand this and embrace the challenge with an adventurous spirit. In order to develop user engaging NLP-based features, it is necessary to experiment. Only by doing so will it be possible to surpass technology’s current frontiers.
At Koombea, we believe that being on the look for new ways to implement NLP features in apps is part of the exciting process of innovation that characterizes our company. If you have any ideas or would like to explore ways on how to implement NLP features into your app, contact us for a free consultation.