Introduction to NLP:-
Fans of Star Wars will recognize C-3po, the golden, life-size robot hotelier. While Star Wars takes place in a galaxy far, far away, the possibility of machines speaking to us and answering our questions in a human-like manner is already a reality that is becoming more real by the day. Search engines like Alexa and Echo, as well as smart assistants like them, are examples. Even online calls have one thing in common: robots make them all.

You're probably wondering how they manage to appear so human-like if they're not human. How do they respond so intelligently to me? How do they manage to be so articulate?

This my friends is the magic of Natural Language Processing



Natural Language Processing, also known as NLP, is a branch of artificial intelligence that enables machines to read, understand, and derive meaning from human languages. NLP combines linguistics and computer science to decipher language structure and guidelines and to create models capable of comprehending breakdown and separating significant details from text and speech.

Every day, humans interact with one another via public social media, exchanging massive amounts of freely available data. This information is extremely helpful in comprehending human behaviour and customer habits.

Data analysts and machine learning experts use this data to teach machines to mimic human linguistic behaviour, which saves millions in terms of manpower and time because a person is not always required on the other end of the phone.

Natural Language Processing is also a lot more widespread than you may realise we use it every day in seemingly normal and insignificant situations.



Don’t know how to correctly spell a word, autocorrect has you covered.

If you want to discover if your article or thesis will be tagged for plagiarism, a plagiarism checker will scan the web and find any occurrences of published papers that may match your work line by line.

How does it work?

While NLP appears to be a fun technological concept, it is a cutting-edge and complex technology concept. It's really simple to learn; you start with a paper or an article to help your algorithm comprehend what's going on, and then you turn it into a machine-readable format. This is like forcing a toddler to learn to read for the first time.

Steps :



You begin by segmenting the text, which means breaking it down into its constituent sentences. You can accomplish this by segmenting the article along its punctuation, such as full stops and commas, so that the algorithm can interpret these phrases. We obtain the words in a sentence and explain them to our algorithm individually.




So, we break down our sentences into their constituent words and store them. This is called tokenizing where each word is called a token.




We can speed up the learning process by removing non-essential words that add little significance to our statement but serve to make it sound more unified. Stop words include words like "are", "and", and "the".




Now that we have the fundamental structure of our paper, we must explain it to our computer. We begin by clarifying that some words, such as skipping, skips, and skipped, are the same word with different prefixes and suffixes. This is known as stemming.




We also identify the root words for various words, tenses, moods, gender, and so on. This is known as lemmatization, after the basic word lemma.




By adding these tags to our words, we can now explain to the machine the concept of nouns, verbs, articles, and other components of speech. This is referred to as part of speech tagging.




Following that, we introduce our machine to pop culture references and ordinary names by highlighting names of movies, prominent people or places, and so on that may appear in the document. This is known as entity tagging.


Once we have our base words and tags, we apply a machine-learning approach like Naïve Bayes to train our model, human sentiment, and speech. At the end of the day, the majority of NLP approaches are straightforward grammar techniques that we were taught in school.


NLP Applications:-

Natural Language Processing (NLP) has several practical applications in a wide range of industries. Some of the most common NLP applications are as follows:

1. Sentiment Analysis: NLP can be used to analyse the sentiment or emotion communicated in text data like as reviews, social media posts, and customer feedback. This can assist businesses in better understanding client preferences and increasing customer satisfaction.

2. Chatbots: Natural language processing (NLP) can be used to develop chatbots, which are computer programs that use natural language to interact with humans. Chatbots can be used in customer service to provide support and answer frequently asked inquiries 24 hours a day, seven days a week.

3. NLP can be used to develop speech recognition systems that can translate spoken language into text.

4. NLP can be used to develop machine learning systems that can translate text from one language to another. Machine translation systems translate text using statistical or rule-based approaches.