Understanding NLP(Natural Language Processing) Step by Step
This guide will walk you through NLP concepts in a clear and enjoyable way, perfect for beginners and curious minds.
How computers understand human language
We humans talk and write using words and sentences. But computers don’t understand words like we do. They only understand numbers and codes. So, how can we make computers understand what we say? That’s where Natural Language Processing, or NLP, helps.
NLP is a part of Artificial Intelligence that teaches computers to understand human languages like English, Mandarin Chinese, Hindi, Spanish, or any other language. It helps computers read and work with our words. Now, let’s learn how to use Python to help computers understand language with NLP.
Why Language is Hard for Computers
Computers are very good at working with data that is organized and neat, like numbers in spreadsheets or information in databases. But most of the information we see every day, like news stories, books, or messages on social media, is written in text.
Text is not always neat or simple — it can be confusing, full of slang, mistakes, or different meanings. Because of this, computers find it hard to understand text the way humans do. That’s why we use Natural Language Processing, or NLP, which helps change messy text into clear and organized data that computers can understand and work with easily.
The NLP Pipeline (Simple Steps)
We’ll teach the computer to understand English in steps,
Step 1: Sentence Segmentation
First, we split a big block of text into smaller sentences.
For example, take this text:
"My dog loves to play outside. He runs very fast."We break it into:
Sentence 1: "My dog loves to play outside."
Sentence 2: "He runs very fast."This helps the computer understand one sentence at a time, making it easier to work with the meaning.
Step 2: Word Tokenization
Next, we take each sentence and split it into individual words, called tokens.
For example, the sentence:
"My dog loves to play outside."
becomes:
"My", "dog", "loves", "to", "play", "outside", "."This helps the computer look at one word at a time to understand the sentence better.
Step 3: Part of Speech (POS) Tagging
Now, we tell the computer what kind of word each one is:
Noun (a person, place, or thing): dog
Verb (an action): loves
Adjective (describes something): happyFor example, in the sentence “My dog loves to play,” the computer knows “dog” is a noun, “loves” is a verb, and “happy” would be an adjective if used.
This helps the computer understand how the words work together to make meaning.
Step 4: Lemmatization
Sometimes, words look different but mean the same thing. We change these words to their basic form.
For example:
"dogs" becomes "dog"
"running" becomes "run"So, if the sentence is “The dogs are running,” the computer changes “dogs” to “dog” and “running” to “run.”
This helps the computer understand that different forms of a word still have the same meaning.
Step 5: Stop Words Removal
Some words like “the,” “is,” and “and” appear a lot but don’t add much meaning. These are called stop words.
For example, in the sentence:
"The dog is running and the cat is sleeping,"Words like “the,” “is,” and “and” don’t tell us much, so the computer ignores them.
This helps keep only the important words so the computer can focus better.
Step 6: Dependency Parsing
Now, we teach the computer how words in a sentence are connected to each other. This is like figuring out who is doing what to whom.
For example, in the sentence, “The dog chased the ball,”
The word "dog" is the subject - it's the one doing the action.
The word "chased" is the verb - it tells what the dog did.
The word "ball" is the object - it's the thing that the dog chased.By understanding these connections, the computer can know the meaning of the sentence better. It sees how the words work together instead of just seeing them as separate pieces. This step helps the computer understand who is acting, what action is happening, and what the action is done to, making the sentence clearer.
Step 6b: Noun Phrases
Sometimes, several words together form one complete idea. This group of words is called a noun phrase.
For example, if we say,
"the big brown dog,"all these words together describe one thing — the dog. Instead of looking at each word separately, the computer learns to treat “the big brown dog” as one unit.
This helps the computer understand better because it sees the full idea, not just single words. Grouping words like this makes it easier to understand sentences, just like when we read or listen to someone talking.
Step 7: Named Entity Recognition (NER)
Named Entity Recognition, or NER, helps the computer find and understand special names in a sentence — like names of people, places, organizations, or dates.
Let’s say we have this sentence:
"Tommy the dog visited New York on July 18."NER helps the computer find:
"Tommy" → Person (or in this case, the name of the dog)
"New York" → Location (a place)
"July 18" → Date (a specific time)This is helpful when we want to search for important information or summarize big text quickly. Instead of reading everything, the computer can pick out key details like who, where, and when.
Step 8: Coreference Resolution
When we talk or write, we often use words like he, she, it, or they instead of repeating the same name again and again. This makes our language sound more natural. But computers don’t always know who or what these words are talking about.
That’s where coreference resolution comes in — it helps the computer figure out what these pronouns refer to.
Let’s look at an example:
"Tommy is a friendly dog. He loves to play fetch."Here, we know that “he” means “Tommy” because we’re humans and understand the context.
But for a computer, this isn’t so easy. It needs to learn that “he” is referring to the dog Tommy.
Coreference resolution teaches computers to connect the dots so they understand the sentence better — just like we do when we read or listen.
Step 9: How Do Computers Understand the Meaning of Words?
Humans can easily tell that some words are related. For example, we know that “happy” and “joyful” are similar in meaning, or that “king” and “queen” are related. But how can a computer understand things like that?
To help computers understand word meanings, we use something called word vectors (also known as word embeddings). These are sets of numbers that represent each word in a way that captures its meaning.
Let’s go back to our dog example:
"Tommy is a friendly dog. He loves to run and play."Using word vectors, the computer can learn that the word “dog” is similar to words like “puppy”, “pet”, or “animal” because they appear in similar types of sentences. It can also learn that “run” and “play” often go together when talking about dogs, kids, or sports.
So, even though computers don’t feel or think like humans, these special number-based word meanings help them get a better sense of what we’re talking about — like how Charlie the dog enjoys having fun.
Step 9b: Word2Vec — Turning Words into Smart Numbers
Let’s say we want a computer to really understand words — not just read them, but know what they mean. That’s where Word2Vec comes in. Word2Vec is a clever tool that turns each word into a set of numbers. But it doesn’t just assign random numbers — it looks at how words appear in sentences and learns what words are related.
For example, if we see these sentences often:
"The dog barked at the cat."
"The puppy played with the child."
"Dogs are loyal animals."Then Word2Vec starts to figure out that words like “dog”, “puppy”, “barked”, and “animal” are somehow connected. It gives each word a word vector (a list of numbers), like this:
"dog" → [0.72, 0.18, 0.90, …]
"puppy" → [0.70, 0.20, 0.88, …]
"cat" → [0.65, 0.10, 0.91, …]Since “dog” and “puppy” are close in meaning, their numbers are very similar. That’s how the computer “knows” they’re related — the numbers are close!
Even cooler? We can do math with these words:
dog — puppy + kitten ≈ cat
Or in another case:
king — man + woman ≈ queen
This works because Word2Vec captures real meaning based on how words are used in sentences. So, by turning words into smart numbers, computers can start to understand language more like humans do!
Step 10: Finding similar words
Now that we have word vectors, we can ask the computer:
What words are similar to "dog"?It might answer:
"puppy"
"pet"
"hound"This is helpful in search engines or chatbots.
Step 11: Training Your Own Word2Vec
You don’t always have to use Google’s word vectors or someone else’s data. You can train your own Word2Vec model using your own text — and that can be really helpful!
For example, imagine you have thousands of articles or stories about dogs. These might include words like:
- “puppy”, “bark”, “tail”, “leash”, “walk”, “treat”, “vet”, “obedience”, and so on.
When you train Word2Vec on just these dog-related texts, the computer starts to learn how these dog words are connected.
So:
"puppy" might be close to "cute"
"bark" might be close to "loud"
"treat" might be close to "reward"This is great because it helps the computer understand your special topic better. Whether you’re working on dogs, food, science, or sports — training your own Word2Vec means it learns your language in the way that matters most to you.
Step 12: How NLP Helps in Real Life
Now that we’ve learned how NLP works, let’s see how it’s used in real life:
- Chatbots — Talk with people through messages or voice (like customer support).
- Search engines — Help understand what you really want when you type something.
- Spam filters — Catch and remove unwanted or junk emails.
- Voice assistants — Like Siri or Alexa, they listen to you, understand your words, and talk back.
- News summarizers — Read long news articles and give you short, simple summaries.
NLP helps computers read, understand, and talk like humans — which is super useful in many apps we use every day!
Natural Language Processing, or NLP, is a fun and powerful part of computer science. It helps computers understand human language — like what we say or write. This is how apps like Google Translate, Siri, or even chatbots can talk and respond like people.
With Python and tools like spaCy or Word2Vec, we can teach computers to read text, find meaning, and even reply in smart ways. Step by step, we break down sentences, find the important words, and figure out how everything fits together.
Even though computers don’t speak like us, NLP gives them a way to understand. And the best part? You don’t need to be a genius to get started — it’s really fun to build programs that can talk or understand stories.
Once you learn the basics, you can make your own chatbot, build a smart search tool, or help computers read books and give summaries. NLP turns regular text into something computers can actually work with. That’s pretty amazing!
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