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10 minutes read

Natural Language Processing Techniques for Apps

By Jonathan Tarud
By Jonathan Tarud
10 minutes read

The evolution of Artificial Intelligence (AI) has enabled the development of new Natural Language Processing techniques. As computer programs become more sophisticated, combined with the introduction of Machine Learning models, the ability to interpret human language and perform syntactic analysis has increased exponentially. This has allowed computer scientists to explore a wide range of computational linguistics topics. 

The study of natural language understanding from a computer science perspective has baffled experts for many years. Yet, even though we better understand how it works, language is still a mystery to be solved. 

A computer system doesn’t exist that can process language as human brains do. After all, this seems to be one of the most critical and complex characteristics of being human. 

Even with just a fraction of the power of humans to process language, apps in several different categories are starting to implement essential language processing techniques to give users all sorts of features, such as text summarization. In this post, we explain what Natural Language Processing (NLP) is, and most importantly, we discuss some of its uses for app development. 

What is Natural Language Processing?

Just as computers make sense of different types of data, it is possible to make sense of language. After all, language is just another form of data. However, for machines to understand language, computers first need to interpret it and transform it into something they can understand. 

NLP is the text classification process through which a computer processes what is being said, determines its context, gives sense to it, and responds based on the resulting meaning. In short, it is the way computers make sense of human language. 

The NLP process resembles something like this:

  1. Voice output is reproduced for the computer. 
  2. The computer receives the input and registers it.
  3. The registered information is transformed into text. 
  4. The text is processed through a neural network that gives ‘meaning’ to it. 
  5. Based on the resulting ‘meaning,’ the computer executes a subsequent command. 

This sounds relatively easy, but a lot is going on regarding how computers give meaning to any type of text. For example, different code structures can interpret the exact same text in different ways. This occurs because computers cannot quickly determine the context of language like humans do. 

Therefore, a neural network must do so; a neural network is a supervised form of Machine Learning that uses deep learning algorithms to make sense of information. The inability to interpret the context correctly is one of NLP’s main struggles to make sense of human language. 

To make sense out of human language, computers try to establish relations between words. Then, by performing semantic analysis, they can figure out what a given structure says. In other words, determining the syntax and semantics of a language structure, even superficially, is essential for NLP to work correctly. 

Natural Language Processing Techniques

The following techniques all use Deep Neural Networks and the latest Machine Learning methods. Although each is different, they can all be implemented into an app depending on what is needed. They are not the only techniques available for NLP, but they are the most common tasks this technology can perform. 

Word Embedding

Word Embedding is a Neural Network technique used to represent the text data within a document. For example, words are represented in vectors to quantify and categorize the relations and similarities between different linguistic elements. The result is a map that puts into context the terms of a document. These Machine Learning algorithms are potent, but they require using named entity recognition to avoid inefficiencies and perform keyword extraction. 

Convolutional Neural Networks

Convolutional Neural Networks are powerful tools for image or video recognition. The organization of the network is inspired by biological research, as it resembles the animal visual cortex. This technique uses linear mathematical operations known as convolutions in which the shape of a function is analyzed given the changes in a related function. It is mainly used for face and object recognition.

Recurrent Neural Networks

Also known as RNNs, this class of Neural Networks is one of the most popular NLP techniques. However, it is mainly used for speech recognition tasks because it allows previous outputs to be used as inputs by storing them for an arbitrary duration.

This is very helpful for defining the context in language. RNNs are also popular because the size of the model does not increase with each new input. RNNs can process any input, no matter its length, while taking historical information into account throughout the process. 

RNNs also have some disadvantages. For a start, they are a backward-looking technique, thus limiting their use for predictions or natural language generation. Additionally, they can be slow as they calculate and process information through named entity recognition, especially for unstructured data. 

Long Short-Term Memory (LSTM) networks are a type of RNN mainly used for sequence prediction problems. However, they solve many of the issues encountered by traditional RNNs.

Gated Recurrent Unit Networks are another type of RNN that perform well in sequence learning tasks. For example, they are sometimes used to analyze financial time series and make predictions. 


This technique is trending in the industry. It is replacing RNNs thanks to its relative simplicity. RNNs require additional attention mechanisms; that is, they need to know what to focus on. Transformers, on the contrary, do not need the extra attention mechanism. This makes them a preferred alternative as they have a similar performance as RNNs while being easier to use. 

Transformers are similar to LSTMs because they help transform a sequence by using an Encoder and a Decoder. One of the most popular transformers is BERT, a language representation model. This can be used for tasks like question answering and language inference. 

Setting Up NLP Features

Before implementing your app’s features, it is vital to keep in mind that, like other data-dependent technologies, NLP first needs to be correctly set up and given the proper training data to work correctly. Failing to establish the correct 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 science, it is crucial to ensure that everything related to how language-based data is collected, processed, and stored is aligned with your app’s needs. Therefore, 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 simultaneously. 

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. For example, placing a phone call, sending a message, or playing a song using your voice are all actions that fall under this category. 

Sentiment Analysis

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. For example, through a Sentiment Analysis, it is possible to analyze a text and interpret a user’s emotions 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. For example, a natural language generation algorithm can identify a user’s writing style 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 quickly. 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 NLP voice recognition techniques, algorithms can help make sense of speech, translating it to written text. Thus, speech-to-text conversion is particularly helpful in improving accessibility.  


Search can be considered a particular type of request, but it often receives its own mention because it is so commonly used. 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 browse files using speech rather than a mouse or a keyboard. 

Text Generation

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. But, for the moment, it remains a work in progress. 

The world was surprised by Open AI’s GPT-3 text generator (GPT stands for Generative Pre-trained Transformer). It is a powerful AI tool that excels at writing texts that can fool people into believing that another human wrote them. As good as it sounds, GPT-3 also has critics. Many argue that it is incapable of making sense of information. 


Voice translation is like a holy grail for many, and it is understandable. If computers were able to translate spoken languages in real-time, it would open up many possibilities. Machine translation remains a work in progress, but vast improvements have been made to language detection tools. Machine translation tools with solid linguistic knowledge are already available, but they aren’t yet seamless. 

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. But, again, this is another area where bots have much to gain. 

Final Thoughts

Natural Language Processing systems are undergoing rapid and constant transformation. It is hard to keep up with each new technique that arises, but that should not be a reason to avoid implementing it in your app. If you are interested in implementing the latest Natural Language Processing techniques in your app, reach out to an app development partner.

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