Understanding NLP: The Fusion of Linguistics, Computing, and Deep Learning

In today's data-driven world, where APIs and diverse data sources power applications, Natural Language Processing (NLP) plays a crucial role in enabling seamless human-computer interactions. From search engines and language translation to chatbots, sentiment analysis, and virtual assistants, NLP is at the core of modern applications, transforming the way machines understand and process human language.

But what exactly is NLP, and why is it so powerful? Let’s break it down.

The Three Pillars of NLP

NLP is a blend of three key elements:

L – Linguistics: The study of language and its structure.
P – Processing: The computational techniques used to analyze and interpret language.
N – Neurons: Inspired by the human brain, deep learning models use artificial neurons (or perceptrons) to process language efficiently.

This intersection of linguistics, computer science, and deep learning enables NLP to extract meaning from text and power a wide range of intelligent applications.

How Computers Understand Language

Unlike humans, computers don’t naturally understand language. Instead, they rely on text processing techniques to break down and interpret words. Some fundamental concepts include:

Stemming – Reducing a word to its root form using basic rules. Example: running → run.
Lemmatization – A more sophisticated way of reducing words using semantic meaning. Example: better → good.
Stop Words – Common words (the, is, and, of, etc.) that don’t add much meaning or does not contribute and are often removed to improve text analysis.

A Fun NLP Joke

Before learning NLP text processing, I was like a stemmer—just chopping things off without thinking. But after learning lemmatization, I finally analyze and understand before taking action. Now, I don’t just do things—I know why I’m doing them! 😆

We’ve all heard about recent layoffs at major tech companies like Microsoft and Meta due to employee performance reviews. But since both companies invest heavily in NLP models, does that mean they’re applying the STOP WORDS technique to their workforce? 🤔

Think about it—employees go through multiple rounds of interviews, and after using a Softmax activation function, they are hired. Now, if they’re getting laid off based on "lack of contribution", doesn’t that contradict the hiring model? Maybe the HR algorithm needs some fine-tuning! 😆

Getting Hands-on with NLP in Python

If you’re interested in NLP, Python provides two powerful libraries:

NLTK – The Natural Language Toolkit, a comprehensive library for text processing.
spaCy – A fast and efficient NLP library for large-scale text analysis.

Tokenization: The First Step in NLP

Tokenization is the process of breaking text into individual words or sentences. Let’s use NLTK to tokenize a simple sentence:

import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')

text = "My name is Sreeni Ramadurai. I am from India. I am a Cloud and GenAI SA, working at ProVizient."
print(word_tokenize(text))

Output:

['My', 'name', 'is', 'Sreeni', 'Ramadurai', '.', 'I', 'am', 'from', 'India', '.', 'I', 'am', 'a', 'Cloud', 'and', 'GenAI', 'SA', '.', 'I', 'am', 'working', 'at', 'ProVizient', '.']

Now, let’s build a simple NLP pipeline for sentiment analysis using NLTK, spaCy, and Scikit-Learn.

Building a Basic NLP Pipeline

Install the required libraries:

pip install nltk spacy scikit-learn

Now, let’s create a sentiment classification model:

import nltk
from nltk.tokenize import word_tokenize
import spacy
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from nltk.corpus import stopwords   

# Download stopwords
nltk.download("stopwords")

# Sample dataset
text = [
    "I love India",
    "I love India, I love my country",
    "I love my country",
    "I love my country India",
    "I hate cold weather",
    "I hate hail",
]

# Labels: 1 = Positive Sentiment, 0 = Negative Sentiment
labels = [1, 1, 1, 1, 0, 0]

# Load spaCy NLP model
nlp = spacy.load("en_core_web_sm")  

# Tokenizer function using spaCy
def spacy_tokenizer(sentence):
    return [word.text for word in nlp(sentence)]

stop_words = stopwords.words("english")

# Building the NLP pipeline
pipeline = Pipeline([
    ("vectorizer", CountVectorizer(tokenizer=spacy_tokenizer, stop_words=stop_words)),
    ("classifier", MultinomialNB())
])

# Train the model
pipeline.fit(text, labels)

# Test the model with new data
new_text = ["I love the current government in India", "I hate Dead Mindset Koottam"]
print(pipeline.predict(new_text))

Note

CountVectorizer is a Bag-of-Words (BoW) technique that converts text into a numerical matrix. It creates a vocabulary from the text and represents each document as word #counts, ignoring word order and meaning. I will write separate Blog to explain BOW Blog Of its Own

Output:

[1, 0]

The model correctly predicts positive and negative sentiments based on the text!

Conclusion

NLP is transforming the way we interact with technology. By blending linguistics, computing, and deep learning, NLP enables powerful applications like chatbots, language translation, and sentiment analysis.

With libraries like NLTK and spaCy, getting started with NLP in Python is easier than ever. So, whether you’re analyzing sentiment, building a chatbot, or just having fun with text processing, the world of NLP is full of exciting possibilities! 🚀

What’s your favorite NLP use case? Let’s discuss in the comments! 💬

Thanks
Sreeni Ramadorai