Natural Language Processing (NLP): The Complete Foundation Guide

The global NLP market is projected to reach $112B by 2030 (Allied Market Research). This tutorial covers fundamental techniques through modern transformer architectures that power applications like ChatGPT and Google Translate.

NLP Application Distribution (2024)

Chatbots/Virtual Assistants (35%)

Sentiment Analysis (25%)

Machine Translation (20%)

Other (20%)

1. NLP Fundamentals

Core Techniques:

Tokenization: Splitting text into words/subwords
Stemming/Lemmatization: Reducing words to base forms
Stopword Removal: Filtering common words
POS Tagging: Identifying grammatical roles

Python Implementation:


import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")

# Tokenization and POS tagging
for token in doc:
    print(token.text, token.lemma_, token.pos_)
    
# Named Entity Recognition
for ent in doc.ents:
    print(ent.text, ent.label_)

2. Text Representation

Evolution of Methods:

Technique	Description	Dimensionality
Bag-of-Words	Word frequency vectors	Vocabulary size (10⁴-10⁶)
TF-IDF	Weighted frequency	Vocabulary size
Word2Vec	Dense embeddings	300-1000
BERT	Contextual embeddings	768-4096

Creating Embeddings:


from gensim.models import Word2Vec
from sklearn.feature_extraction.text import TfidfVectorizer

# Word2Vec
sentences = [["natural", "language", "processing"], ["deep", "learning"]]
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1)

# TF-IDF
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(["This is NLP tutorial", "Deep learning for NLP"])

3. Modern NLP Architectures

Key Models:

RNNs/LSTMs

Sequential processing

Legacy approach

Transformers

Attention mechanisms

Current SOTA

BERT/GPT

Pretrained models

Transfer learning

HuggingFace Implementation:


from transformers import AutoTokenizer, AutoModel

# Load pretrained BERT
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

# Process text
inputs = tokenizer("Hello world!", return_tensors="pt")
outputs = model(**inputs)

NLP Task Cheat Sheet

Task	Example	Model Choice
Text Classification	Sentiment analysis	BERT, DistilBERT
Named Entity Recognition	Extract people/places	spaCy, BERT
Text Generation	Chatbots, stories	GPT-3, T5
Machine Translation	English to French	MarianMT, mBART

4. NLP Applications

Industry Implementations:

Search Engines: Semantic search with BERT
Customer Service: Intent detection in chatbots
Healthcare: Clinical note analysis
Finance: Earnings call sentiment

Complete Text Classification:


from transformers import pipeline

# Zero-shot classification
classifier = pipeline("zero-shot-classification", 
                     model="facebook/bart-large-mnli")
result = classifier(
    "This tutorial explains NLP techniques",
    candidate_labels=["education", "politics", "business"]
)
# {'labels': ['education', 'business', 'politics'], 'scores': [0.95, 0.03, 0.02]}

NLP Learning Path

✓ Master text preprocessing

✓ Understand word embeddings

✓ Experiment with transformers

✓ Fine-tune pretrained models

✓ Build end-to-end applications

NLP Researcher Insight: The 2024 ACL survey shows that 90% of production NLP systems now use transformer-based models, with parameter-efficient fine-tuning (PEFT) techniques like LoRA reducing compute costs by 80%. Modern best practices emphasize prompt engineering for LLMs and synthetic data generation for domain adaptation.

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