Technology 7 min read

NLP Applications: How They Work and What They’re Used for

Iurii Motov /

Iurii Motov /

Main NLP Applications Takeaway:

  • NLP is an AI subfield that explores the link between computers and human language.
  • The study of natural language processing started as far back as the 1950s.
  • Artificial Intelligence is a general term for machines that can mimic human intelligence.
  • Machine learning and natural language processing are subsets of Artificial Intelligence.
  • Chatbots and virtual assistants rely on NLP models to function.
  • Machine Learning involves using algorithm that can learn and adapt from experience.
  • Businesses use NLP to automate support and improve user experience.
  • Some NLP applications include machine translation, sentiment analysis, keyword detection, and text extraction.

We rely on natural language processing for the simplest things today. Whether dealing with survey responses or replying to online chats, the AI subfield serves different purposes in our businesses and personal lives.

What’s more, NLP applications are more advanced than ever due to recent breakthroughs in machine learning.

While some of its uses may seem confusing to the uninitiated, this post aims to simplify things. Here’s a breakdown of what NLP applications are and how we use them.

What is NLP and its Applications?

Natural Language Processing or NLP is a subfield of artificial intelligence that deals with the link between computers and human language. NLP involves programming computers to process and analyze a massive amount of natural language data. And that results in applications that understand document content and the contextual nuances of the language within them.

The study of natural language processing started as far back as the 1950s. However, the field has evolved due to significant breakthroughs in a Machine Learning subfield called Deep Learning (DL).

Thanks to this Machine Learning advancement, we now have NLP applications such as semantic analysis and text classification. There’s also Machine Translation, speech recognition, and keyword extractions.

Yes, it all sounds a little confusing. First, let’s clarify things.

What is the Difference Between NLP, AI, and Machine Learning

Blog posts sometimes use the term Natural Language Processing (NLP), Artificial Intelligence (AI), and Machine Learning (ML) interchangeably. And that might not be clear to readers. The first thing to note is that AI is a general term for machines that can mimic human intelligence. These include systems that can learn from examples, solve problems, and predict potential outcomes. On the other hand, both machine learning and natural language processing are subsets of Artificial Intelligence.

NLP focuses on how computers understand and translate the human language.

With an understanding of the spoken and written text, systems can perform tasks such as speech recognition. Also, chatbots and virtual assistants rely on Natural Language Processing models to function.

However, NLP applications are even more useful when they’re automated and provide accurate responses. And that’s where machine learning comes in.

Machine Learning (ML) involves using algorithm that can learn and adapt from experience instead of being explicitly programmed.

For example, Amazon’s Alexa uses NLP to interpret and extract information. However, the digital assistant also uses ML to deliver accurate responses by learning from past interactions.

Where is NLP Used?

Natural Language Processing is useful for businesses that are looking to improve their operations. Indeed, large companies that want to track, facilitate, and analyze thousands of customer interactions can benefit from the AI subset. Along with monitoring feedbacks, businesses use NLP to automate support and improve user experience.

Social media platforms such as Facebook also use NLP to improve targeted advertising. Other areas where natural language processing has proven useful are:

  • Related keywords on search engines
  • Speech recognition in Alexa, Siri, and Google Assistant
  • Spam filters in Gmail
  • Content relevance analysis in INK
  • Voice text messaging
  • Predictive typing on Google

As you can see, NLP has become an essential part of our daily lives.

At this point, you have to be wondering how it all works. In other words, how exactly do the products above use this AI subset?

What are NLP Applications?

Speech Recognition may be the most popular NLP application. It involves using natural language processing to convert spoken language into a machine-readable format. Besides being useful in virtual assistants such as Alexa, speech recognition technology has some businesses applications. For example, speech-to-text programs are useful for transcribing calls, send emails, and translate. Other NLP applications include machine translation, sentiment analysis, keyword detection, text extraction, etc.

1. Machine Translation

As in speech recognition, Machine Translation is one of the first applications of natural language processing.

Machine translation or MT is a sub-field of computational linguistics that involves translating text or speech from one language to another. So, it’s a quick way to improve communication between multiple parties.

Also, businesses use machine translation to reach a broader audience and understand foreign documents.

One primary challenge with machine translation was the understanding of context. However, that’s gradually changing due to advances in neural networks and access to more training data.

For example, programs such as Google Translate have undergone significant improvement since its inception.

Sentiment Analysis

Sentiments such as irony and sarcasm are an essential part of human interaction. As such, computers should find it difficult to understand these subtle nuances in emotions and opinions.

However, an NLP application called sentiment analysis makes it possible for machines to grasp human opinions.

Also known as opinion mining, sentiment analysis helps determine whether data is positive, negative, or neutral. Businesses use this NLP technique on textual data to monitor product sentiments.

Other applications of sentiment analysis include:

  • Monitoring mentions on social media
  • Gauging brand sentiment
  • Understand customer needs

Performing periodic sentiment analysis on social media could help catch and handle negative comments before they escalate. It also provides an excellent way to generate catchy blog post titles.

For instance, web writing applications such as INK use sentiment analysis to suggest ways that writers can add more emotions to headlines.

Text Classification

Text classification is the process of understanding, processing, and classifying unstructured text. It involves assigning tags and categorizing text based on its content.

From the definition, you can safely assume that sentiment analysis is only possible because of text classification. However, this NLP application also extends to spam detection, topic labeling, and even intent detection.

For example, manually analyzing hundreds of open-ended responses in a survey can be time-consuming and expensive. Instead, you could train a text classification model to tag your data using predefined categories.

Other names for text classification are text categorization and text tagging.

Text Extraction

Text extraction — also known as information extraction — is an NLP technique for collecting specific information within a document. These include a person’s name, place, email address, or phone number.

Computer programming professionals use five other NLP methods for text extraction. These include:

  • Named entity recognition
  • Sentiment analysis
  • Text summarization
  • Aspect mining
  • Topic modelling

For example, businesses sometimes use text extraction models to automate data entry tasks. After pulling out the specific data, you could set up a trigger to automatically add the information to your database.

Text extraction can also provide insights for effective content creation. For example, Google related question applies this NLP technique to identify and understand searcher’s questions and intentions.

Marketing Intelligence

Market intelligence may be one of the most popular natural language processing applications today.

The NLP application uses text extraction, sentiment analysis, and intent extraction to analyze unstructured data. That way, marketers can identify trends and potential business opportunities.

Market intelligence is also useful for analyzing data to identify customer’s needs and competitor’s strengths. That way, you’ll have the insight required to develop more effective strategies.

Other NLP applications include text summarization, autocorrect, intent classification, autocorrect, among others.

Wrapping Up

Natural Language Processing applications provide a seamless way for humans to interact with machines.

Admittedly, this artificial intelligence subfield has been around for decades. However, the latest research breakthroughs have taken the NLP applications to the next level.

Now, we have programs that can understand, assess, and synthesize text and speech in unprecedented ways. Besides, the advancement opened the door to several exciting applications.

For example, businesses now rely on NLP to process massive amounts of unstructured data.

These include social media posts, survey responses, support tickets, among others. Search engines such as Google and Bing also use NLP models like BERT to understand search queries better.

Read More: 8 Examples of Artificial Intelligence in our Everyday Lives

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Sumbo Bello is a creative writer who enjoys creating data-driven content for news sites. In his spare time, he plays basketball and listens to Coldplay.

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