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How to Process Natural Language Techniques for Apps

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The evolution of Artificial Intelligence (AI) has given rise to many subfields of computer science. As computers become more sophisticated, and thanks to the introduction of Machine Learning techniques, the ability to process information has increased exponentially. This has allowed computer scientists to explore a wide range of topics. Language has not been exempt from this process, and some of the world’s best apps have taken notice.

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The study of language from a computer science perspective has baffled experts for many years. Even though we now have a greater understanding of how it works, language is still a mystery to be solved. There doesn’t exist a computer that is able to process language as human brains do. After all, this seems to be one of the most important and complex characteristics of being human.

Even with just a fraction of the power of humans to process language, apps are starting to implement important language processing techniques to give users all sorts of features. 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, in order to make sense out of it, computers first need to interpret it and transform it into something they can treat and understand. NLP is the 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 rather easy, but there is a lot going on in terms of how computers give meaning to any type of text. Different code structures can interpret the same text in different ways. This occurs because computers cannot easily determine the context of language just like humans do. Therefore, it is very important that a neural network does so; a neural network is a supervised form of learning that uses deep learning algorithms to make sense of information. The inability to interpret the context correctly is one of the main causes of NLP failing to make sense of human language.

To make sense out of human language, computers try to establish relations between words. By doing so, they can figure out what a given structure says. In other words, determining the syntax and semantics of a language structure, even at a superficial level, is essential for NLP to work properly.

Natural Language Processing Techniques

The following techniques all use Deep Neural Networks. 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 sure are the most popular and potent ones.

Word Embedding

Word Embedding is a Neural Network technique used to represent the vocabulary within a document. Words are represented in vectors in order to quantify and categorize the relations and similarities between different linguistic elements. The result is a map that puts into context the words of a document. These algorithms are very powerful, but they require using the right tools in order to avoid inefficiencies.

Convolutional Neural Networks

Convolutional Neural Networks are very strong for image and 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 mostly 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. It is mostly used for speech recognition, as it allows the use of previous outputs as inputs by storing them for an arbitrary duration. This is very helpful for defining the context in language and also because the size of the model does not increase with each new input. A major advantage RNNs show is that they can process any input, no matter its length, while taking into account historical information throughout the process.

RNNs also have some disadvantages. For a start, they are a backward-looking technique, thus limiting their use for predictions. Additionally, they can take time to calculate and process information, especially when it comes to old data.

Long Short-Term Memory (LSTM) networks are a type of RNN mostly used for sequence prediction problems. They solve many of the problems encountered by traditional RNN, particularly in how the model is trained.

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


At the time of writing, 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 where to focus on. Transformers, on the contrary, do not need the additional attention mechanism. This makes them a preferred alternative as they have a similar performance as RNNs while being relatively 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.

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