Do you know what natural language processing entails? Well, NLP’s long-term objective is to provide computers with the same linguistic skills we possess. Furthermore, NLP is the engine that powers AI-powered tools like speech recognition, virtual assistants, machine translation, etc.
Not many people know, but the NLP industry’s global estimate for 2030 is a whopping $112.28 billion. Isn’t that unbelievable? It is because NLP, with AI’s help, helps make more informed business decisions.
In this article, we will have a look at NLP’s fundamentals and the way it works, besides diving into its applications and techniques, so read on!
4 Applications of NLP
Let us examine some Natural language processing examples and see how they reduce our work and efficiently finish some tedious jobs.
1. Filtering emails
Firstly, you do know how important emails are in our everyday life. The emails you receive can be job-related or purely promotional. Moreover, in such cases, NLP comes into play as it filters and identifies the incoming mails and flags them as “spam” and important.
2. Predictive text
Besides, web searches and predictive text go hand in hand. It is an integral part of your mobile typing experience. In addition, after you have typed a few words, the keyboard suggests the next word learning from your typings. NLP’s use allows the cellphones to suggest these suggestions based on your usage and texting patterns.
3. Chatbots
One of the Natural language processing examples includes chatbots, with everything has gone digital. You get accurate and rapid responses because of the chatbots and save time. Moreover, NLP equips the chatbots with conversational skills that permit them to provide nuanced responses to customers.
4. Text Mining
Additionally, Text mining implies the extraction of useful information from a great deal of unstructured text. It is this automated technique that makes use of NLP. Text mining automates the text classification process by intent, topic, and sentiment. How does NLP do that? By further transforming the data into comprehensible information for machines.
Natural Language Processing Examples and Techniques
Now that you know NLP’s various applications, let us dive into some of the popular NLP techniques. Moreover, some of these techniques are interdependent and can be completed as a part of a bigger task.
1. Stemming
Similarly, stemming is one of the NLP techniques, which is more of an efficiency and pre-processing strategy. Additionally, words can be broken or “stemmed down” to their root forms for multiple purposes. Also, are you thinking as to why we need this stem? Well, you need a stem as you will encounter a word’s different variations. For instance:
“She was taking a car ride.”
She was riding a car.
Since these 2 sentences mean the same thing, the word’s use is identical.
2. Text Segmentation
To emphasize, it is one of the NLP techniques which helps to break down sentences and individual words into more manageable chunks. Depending on the language, this whole separation process of text into individual words can be a challenging task, but NLP makes it effortless.
Advantages and Disadvantages of NLP
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Conclusion
In conclusion and by now, you must have understood that NLP’s reach has extended to nearly every industry. So what are you waiting for? Furthermore, assist in reducing your manual labor and maximize your productivity today!
FAQs
1. What are NLP’s 5 steps?
The 5 steps of NLP include:
- Pragmatic analysis
- Discourse Analysis
- Semantic Analysis
- Syntactic Analysis
- Lexical Analysis
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2. Where is natural language processing used?
NLP is at every tool’s core. You use them every day in either search engines, spam filters, chatbots, or voice assistants.
3. What is NLP’s working principle?
NLP uses AI to take input from the real world and further process it. Later, it makes sense of that in such a way that computers can easily understand those inputs.
