5 Real Life Use Cases of Natural Language Processing (NLP)

October 26, 2020

Language as a method of communication helps people write, read, and speak. Individuals make plans and decisions in natural language, particularly in words. The current era of artificial intelligence is very keen to determine if individuals can communicate the same way with computers.

To ensure that human beings communicate with computers in their natural language, computer scientists have developed natural language processing (NLP) applications. For computers to understand unstructured and often ambiguous human speech, they require input from NLP applications.

Understanding Natural Language Processing

The 21st century is characterized by the automation of systems and gadgets. Moreover, the use of computation is moving to elevated levels. The automation of things that we use daily is meant to make our lives easier and better. Examples of automation products include self-driving cars, google assistants, and artificial intelligence within software systems.

Each of these technologies has deep learning, machine learning, or artificial intelligence technologies. Today people embrace gadgets that rely on machine learning or artificial intelligence, due to their popularity, and their prolific use within data science, cloud computing, natural language processing, data engineering, computer vision, and more.

For a more in depth look at deep learning, machine learning, and artificial intelligence refer to this article.

Natural Language Processing (NLP) is a type of AI that seeks to enable computers to process or understand human language. Ideally, NLP does this by programming computers to analyze and process large quantities of natural language data. It can manipulate speech and text through computational power enabled by various software.

Large amounts of data contain hidden information. The data mining process helps extract hidden relationships and patterns in large data sets. Much of this data is unstructured and composed of speech, videos, text, images, and more.

Businesses are interested in text data because it contains information relating to marketing media, pricing playbooks, product documentation, business contracts, etc. Natural processing language applies techniques to extract patterns in textual data from large datasets.

Natural Language Processing (NLP) vs Natural Language Understanding (NLU) vs Natural Language Generation (NLG)

Natural language processing contains algorithms that help with speech recognition. NLP systems also rely on neural networks to classify texts, answer questions, and perform sentimental analysis. A part of NLP is natural language understanding (NLU). In NLU, the program understands, finds meaning, and performs a sentimental analysis.

This is made possible with the program breaking the language down into small bits that are more easily understood. It also involves an analysis of the text, both syntactically and semantically.

Once a smart device, search function, or chatbot understands the language, it has to talk back in a manner that users will understand. This relates to natural language generation (NLG), and its responsibility is to take data from a search result and turn it into understandable language.

NLP vs NLU vs NLG

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Applications of NLP

Checking Grammar

One of the most widely recognized uses of natural language processing is grammar checking. Grammar checkers detect and correct grammatical errors in the input text. They help in language learning, text authoring, and other scenarios.

While we can still check our error manually (to double check). A grammar checker would be more effective and faster to note any major grammatical mistakes, misspellings, and it would provide suitable suggestions to correct these errors.

Writing tools, including Grammarly, WhiteSmoke, and ProWritingAid, rely on the use of NLP to correct grammatical and spelling errors. Grammarly primarily focuses on a narrow application of artificial intelligence NLP for grammar assistance.

Besides grammatical assistance, these tools check the clarity of the text and suggest better synonyms. With these tools, you are sure to improve your texts’ readability and be able to convey your message appropriately. Grammar checkers have continued to introduce new features, and a tool like Grammarly allows you also to perform a plagiarism check.

Grammar Check Using Grammarly

Chatbots

Strong customer support is a goal for every business. Engaging employees to address customers’ demands, day and night may pose various challenges. That is why virtual assistants and chatbots exist today. Chatbots are replacing traditional 24/7 support and companies’ FAQs. We rely on NLP to train chatbots to behave in a certain way. Efficient chatbots interact and converse with people the way a human being would.

Users that are using chatbots may not realize that they are not chatting with a real human due to the perfect impression they may get. For these chatbots to work efficiently, they need the capacity for efficient natural language generation (NLG) and advanced natural processing capabilities.

With chatbots becoming more content-sensitive and adequate in personalizing users’ experiences, businesses will reap the benefits of an automated 24/7/365 customer support service. In addition, chatbots are acting as virtual assistants being able to respond to any customer complaint.

Search Autocorrect and Autocomplete

Another critical application of NLP is the autocomplete function. If you start your search query on Google, you’ll get many predictions of what you may be interested in (searching) based on the initial few words or characters you entered.

Google Autocomplete and Autocorrect

How does Google suggest relevant keywords? It relies on the data that it catalogs based on what the other millions of Google users are searching for when inputting (similar) search terms. This is possible by using natural language processing that helps understand subtleties between various search terms. NLP also helps make sense of the data.

Autocorrect is another technology that uses NLP technology. You can see the closest possible terms to your misspelled words and change those words with this function.

Machine Translation

Machine translation tools have come to replace the traditional rule-based and dictionary-based language translators. Most machine language tools available today can translate millions of words from one language into another targeted language, that would have been quite challenging the traditional/manual way.

In the current world of globalization, businesses and individuals are taking advantage of language translation tools to break the language barrier and connect to people who speak different languages. All this is possible with the introduction of powerful machines, the availability of data, and evolution in the neural network field.

Language Translation using DeepL Translate

Analytics

Business decisions are difficult to make, and the best decisions are a product of data-driven insights. However, businesses generate large amounts of data, and extracting meaningful insights may take a lot of time. To make quick and effective solutions, business people need to be provided with comprehensive information.

Data generated from insights can be interpreted using natural language generation (NLG) powered tools. Leaders can always consult analytical dashboards that are powered by NLG for the most recent information to help them make the best business decisions.

Many organizations have historically relied on natural language processing (NLP) to perform text analytics and identify data patterns, including contract review and social media sentiment analysis.

Conclusion

Recently, business intelligence and analytic vendors have started to add NLP capabilities to their product offerings. Natural language understanding and Natural language generation, for example, are being used for natural language searches and data visualization narration, respectively.

References

Applications of NLP

Top 10 Applications of Natural Language Processing (NLP)

Language


Peer Review Contributions by: Lalithnarayan C


About the author

Eric Kahuha

Eric is a data scientist interested in using scientific methods, algorithms, and processes to extract insights from both structural and unstructured data. Enjoys converting raw data into meaningful information and contributing to data science topical issues.

This article was contributed by a student member of Section's Engineering Education Program. Please report any errors or innaccuracies to enged@section.io.