Natural Language Processing

100 min

Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language, bridging the gap between human communication and machine understanding. NLP applications include machine translation, sentiment analysis, chatbots, text summarization, and question answering. Understanding NLP enables you to build systems that process and understand text. NLP combines linguistics, computer science, and machine learning to work with human language.

Text preprocessing prepares raw text for analysis by cleaning and normalizing it. Preprocessing steps include removing punctuation, converting to lowercase, removing stop words (common words like 'the', 'a'), handling special characters, and normalizing whitespace. Different tasks require different preprocessing—some tasks need punctuation, others don't. Understanding preprocessing enables you to prepare text effectively. Preprocessing significantly affects model performance.

Tokenization splits text into smaller units (tokens), typically words or subwords. Tokenization is fundamental—it converts text into a format algorithms can process. Different tokenization strategies include word-level (split on spaces), character-level (split into characters), and subword-level (BPE, WordPiece). Understanding tokenization enables you to prepare text for models. Modern tokenizers handle punctuation, contractions, and multilingual text intelligently.

Vectorization converts text into numerical representations (vectors) that machine learning models can process. Traditional methods include bag-of-words, TF-IDF (Term Frequency-Inverse Document Frequency), and word embeddings (Word2Vec, GloVe). Modern methods use contextual embeddings from transformer models. Understanding vectorization enables you to represent text numerically. Good vectorization captures semantic meaning and relationships.

Modern NLP uses transformer models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) for advanced language understanding. Transformers use attention mechanisms to understand context and relationships between words. Pre-trained models can be fine-tuned for specific tasks, achieving state-of-the-art performance. Understanding transformers enables you to use cutting-edge NLP models. Transformers have revolutionized NLP, enabling human-level performance on many tasks.

NLP tasks include classification (sentiment, topic), named entity recognition (finding people, places), machine translation, text generation, question answering, and summarization. Each task requires different approaches and models. Understanding NLP tasks enables you to choose appropriate methods. Best practices include using pre-trained models when possible, fine-tuning for specific domains, understanding evaluation metrics, and considering computational requirements. Understanding NLP enables you to build applications that process and understand human language.

Key Concepts

NLP enables computers to understand and generate human language.
Text preprocessing cleans and normalizes text for analysis.
Tokenization splits text into processable units.
Vectorization converts text to numerical representations.
Transformer models (BERT, GPT) enable advanced language understanding.

Learning Objectives

Master

Preprocessing text for NLP tasks
Understanding tokenization strategies
Vectorizing text for machine learning
Using transformer models for NLP tasks

Develop

NLP thinking
Understanding human language processing
Designing effective NLP systems

Tips

Preprocess text appropriately for your specific task.
Use pre-trained transformer models when possible—they're powerful.
Understand that different tasks require different preprocessing.
Fine-tune pre-trained models for domain-specific tasks.

Common Pitfalls

Over-preprocessing text, removing important information.
Not understanding tokenization, causing model errors.
Using outdated methods when modern transformers would be better.
Not considering computational requirements of large models.

Summary

NLP enables computers to understand and generate human language.
Text preprocessing, tokenization, and vectorization are fundamental.
Transformer models enable advanced language understanding.
Understanding NLP enables language processing applications.
Modern NLP uses pre-trained transformers for best results.

Exercise

Create a simple text classification system using TF-IDF and machine learning.

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report

# Sample text data
texts = [
    "I love this product, it's amazing!",
    "This is the worst purchase ever",
    "Great quality and fast delivery",
    "Terrible customer service",
    "Excellent value for money",
    "Poor quality, don't recommend",
    "Best product I've ever bought",
    "Waste of money, very disappointed",
    "Highly recommend this product",
    "Not worth the price"
]

# Labels: 1 for positive, 0 for negative
labels = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0]

# Split data
X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.3, random_state=42)

# Create TF-IDF vectorizer
vectorizer = TfidfVectorizer(max_features=100, stop_words='english')
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)

# Train classifier
classifier = MultinomialNB()
classifier.fit(X_train_tfidf, y_train)

# Make predictions
y_pred = classifier.predict(X_test_tfidf)

# Evaluate model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")
print("Classification Report:")
print(classification_report(y_test, y_pred))

# Test with new text
new_texts = [
    "This product exceeded my expectations!",
    "I'm very disappointed with this purchase"
]

new_texts_tfidf = vectorizer.transform(new_texts)
predictions = classifier.predict(new_texts_tfidf)

for text, pred in zip(new_texts, predictions):
    sentiment = "Positive" if pred == 1 else "Negative"
    print(f"Text: '{text}' -> {sentiment}")

Natural Language Processing