4 Top Natural Language Processing Techniques to Apply for Mobile Apps

Mobile Apps

Have you ever tried to learn a particular language? 

If yes, we are sure that you know how difficult it is! To learn a language, you have to master grammar and vocabulary. 

Nowadays, more and more businesses are working with hundreds of written and verbal strings every day. 

Being a top mobile software development company, our core focus is on applying Natural Language Processing techniques to move toward a procedure of finding the cognitive meaning behind the traditional keyword-based interpretation. This procedure will help scale your business. Prior to discussing 4 NLP techniques, learn the meaning of natural language processing.

What is Natural Language Processing?

Natural Language Processing is a branch of Artificial Intelligence and Machine Learning that intends to provide computers with the ability to read and understand natural human language.

In short, NLP, a text classification process,  is the way computers make sense of natural human language.

Use Cases of Natural Language Processing

Apart from cancer diagnosis, Natural Language Processing is used in several real-world applications. We have compiled a quick list to help you understand the exciting use cases of Natural Language Processing.

1. Spam Filtering

Google and other popular email providers use Natural Language Processing to identify the most common patterns present in spam to keep them out of your inbox. 

2. Find Fake News

Twitter and other social media channels have started filtering out fake news and focusing on removing it before it is seen by the public.

3. Job Recruitment

The majority of employees have started using Natural Language Processing technique to scan resumes from applicants to pick the right talent that matches job opportunities.

4. Speech Recognition

Google Assistant, Siri, and Alexa are some of the common examples of speech recognition. Along with these, Google Translate also uses Natural Language Processing technique.

Popular Natural Language Processing Techniques You Must Implement

The following Natural Language Processing techniques use the latest Machine Language methods. Although each NLP technique is unique, they all can be implemented in an application based on the requirement.

1. Word Embedding

Word Embedding, a Neutral Network technique, is used to represent words mathematically. One Hot Encoding, FastText, Word2Vec, Binary Encoding, Latest Semantic Analysis Encoding, and TF-IDF are some of the commonly used word embedding techniques in NLP. 

As computers understand the language of numbers, you need to encode words to numbers so that computers can read and process them. 

2. Convolutional Neural Networks

Convolutional Neural Networks are effective tools to recognize images or videos. This NLP technique uses linear mathematical operations (known as convolutions). In this technique, convolutions are used over the input layer to compute the output. This powerful technique is mainly used for face recognition.

3. Recurrent Neural Networks 

Recurrent Neural Networks (also known as RNNs) are one of the most commonly used Natural Language Processing techniques. This NLP technique is used for speech recognition as it allows previous outcomes to be used as inputs. Recurrent Neural Networks can process any input.

Long Short-Term Memory networks are a type of Recurrent Neural Network (RNNs) that solve several issues faced by traditional RNNs. Gated Recurrent Unit Networks are another type of Recurrent Neural Network that are effective in sequence learning tasks. 

4. Transformers

It’s no secret that this NLP technique is trending in the industry. It replaces RNN due to its relative simplicity. The major difference between RNNs and transformers is that RNNs need additional attention mechanisms, but transformers do not need the extra attention mechanism. BERT is one of the popular transformers is BERT that can be used for tasks such as question answering.

Final Thoughts

Natural Language Processing systems are making a rapid transformation. It is challenging to keep up with every latest technique, but you can’t afford to avoid implementing it in your application. We have already uncovered the top Natural Language Processing techniques. Hope you’ll find these techniques helpful for your app.

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