You might have used Google Assistance and said, ‘Hey Google’ and asked your query, interacting with these voice assistants is a kind of fun and learning experience, but you must have been surprised with how these devices understand our language and respond like humans. This is made possible by the technology called natural language processing (NLP).
NLP is a booming field, so let’s dive in to understand what it is and how it is helping businesses grow with its versatile applications.
What Exactly is NLP?
Natural language processing (NLP) is a subset of artificial intelligence (AI). This technology is popularly utilized by personal assistants in a variety of business fields/areas. This technology analyzes the user’s voice, breaks it down for proper comprehension, and processes it accordingly.
NLP is an emerging technology that has already enabled several transitions such as compatibility with smart devices and interactive conversations with humans. The emphasis of AI applications in NLP was on knowledge representation, logical reasoning, and constraint fulfillment.
It was initially used in semantics and then in grammar. A notable change in NLP research over the last decade has resulted in the widespread adoption of statistical methodologies such as machine learning (ML) and data mining on a massive scale.
How is Natural Language Processing Transforming Industries?
The need for automation is never-ending due to the burgeoning amount of work required to be performed these days. When it comes to automated applications, NLP is a beneficial component. Let’s discuss some major applications of NLP.
1. Chatbots
Thanks to chatGPT, chatbots are known to everyone in the world. They are AI systems programmed to interact with humans in such a way that they sound like humans themselves. Depending on their complexity, chatbots can either respond to specific keywords or carry lengthy conversations that make it difficult to distinguish them from humans.
Chatbots employ NLP and ML, which means they understand the nuances of the English language and determine the true meaning of the sentence, as well as learn from human discussions and improve over time. Chatbots function in two simple steps. First, they determine the meaning of the question asked and collect all data from the user needed to answer the query and then, they respond appropriately.
2. Voice Assistants
These days voice assistants have become a craze all around the world. Whether it’s Siri, Alexa, or Google Assistant, practically everyone uses one of them to make phone calls, arrange meetings, create reminders, set alarms, browse the internet, and so on. With the help of NLP, these voice assistants have greatly simplified living. But how do they function?
They understand what humans are saying and then act on it using a complicated combination of speech recognition, natural language understanding, and NLP.
Voice assistants’ long-term goal is to create a bridge between humans and the internet, providing a variety of services based solely on voice commands. However, they still have a long way to go, before Siri starts to understand everything you’re saying!
3. Autocomplete in Search Engines
Have you ever noticed how search engines guess what you are typing and automatically complete your sentences for you? For example, if you type “game” on Google or any search engine, you may get suggestions for “Game of Thrones,” “Game of Life,” or “Game Theory,” in case you are interested in arithmetic.
All of these ideas are generated using autocomplete, which employs NLP to predict what you might ask. Search engines leverage massive data sets to analyze what their customers are likely typing when they enter specific words and give the most prevalent options. They employ NLP to understand how these words are related to generating different phrases.
4. Language Translator
Do you want to translate a text from English to Hindi but don’t know Hindi? Google Translate is the tool you need! Not only it is 100% accurate, it is an excellent tool for converting text from one language to another.
Google Translate and other translation technologies, as well as NLP techniques such as ‘Sequence to Sequence’ modeling, enable the algorithm to translate a sequence of words from one language to another.
Previously, language translators utilized statistical machine translation (SMT), which means they analyzed millions of texts that had already been translated from one language to another. For example, I translated English to Hindi and then looked for common patterns and the basic vocabulary of the language.
However, this method was not as accurate as sequence-to-sequence modeling. Microsoft has employed NLP with its translator, the ‘Text API’. This cloud-based machine translation service helps developers integrate translation capabilities into their services and applications.
5. Sentiment Analysis
Nowadays, almost everyone in the world is on social media! Companies can also use sentiment analysis to learn how a specific type of user feels about a particular topic, product, or service. They can employ NLP, computational linguistics, text analysis, and other techniques to determine users’ attitudes toward their products and services whether it is positive, negative, or neutral.
Companies can employ sentiment analysis in several ways, including determining the emotions of their target audience, understanding product evaluations, gauging brand sentiment, and so on. Not just commercial firms, but governments can also utilize sentiment analysis to discover popular opinion and identify dangers to national security.
During the peak of the COVID-19 pandemic, Google Cloud released its ‘Intelligent Impact Solution’ integrated with a sentiment analysis component. to help government agencies better target their communication efforts and understand how people’s beliefs and behaviors were changing about the COVID-19 vaccination.
6. Email Classification and Filtering
Email is still the most used form of professional communication. However, we all receive thousands of advertising emails that we do not want to read. Fortunately, our emails are automatically divided into three sections: Primary, Social, and Promotions, so we never have to view the Promotions part! But how exactly does this work?
A key part of this is NLP, which scans the content of each email, along with conducting text classification, so that the email can be placed in the appropriate section.
In more advanced circumstances, some businesses utilize specialized anti-virus software with NLP to examine emails for patterns and words that could signal a phishing attempt on employees.
7. Grammar Checkers
Grammar and spelling have become critical today when communication is the bedrock of most interactions, both formal and informal. So it comes as no surprise that grammar and spell checks have become the two major weapons in the arsenal of anyone in the professional realm.
This software can not only check grammar and spelling but also propose better synonyms and improve the general readability of a document. And guess what? They use NLP to enhance language use! The NLP algorithms are trained on millions of texts to understand the correct format. That is why they can suggest a better verb tense, synonym, or sentence structure than what you have typed. Some of the most popular grammar checkers that use NLP include Grammarly, WhiteSmoke, and ProWritingAid, among others.
How is NLP Making RPA Smarter?
In Robotic Process Automation (RPA), NLP analyzes structured, unstructured, and semi-structured documents to identify, extract, and structure data for analysis. NLP applications in RPA include structured and semi-structured document use cases such as invoice processing, insurance claim handling, and contract analysis.
NLP for structured and semi-structured document processing assists businesses in speeding up and automating existing business processes, lowering costs by allowing staff to focus on higher-value tasks. In RPA, NLP is often used for customer review analysis, support issue classification, and several workforce analytics functions.
Expert.ai and Reveal Group established a collaboration to assist organizations in increasing the value of intelligent automation programs through natural language processing and interpretation. By incorporating NLP into RPA, organizations will be able to boost automation’s flexibility and scalability, allowing for the deployment of more complicated use cases and business processes by making sense of unstructured linguistic data.
What’s Next for NLP?
NLP is expected to continue to grow and evolve, with machines becoming increasingly better at understanding and generating human language. Some of the trends that are likely to impact the future of NLP include emotion analytics, which could help vendors build conversational agents that can better understand human emotions, and the use of NLP in finance. Overall, widespread efforts are focused on adopting NLP to further automation in varied sectors and make interactions between humans and computers more effective.
