Understanding Natural Language Processing (NLP): The Bridge Between Human Language and Artificial Intelligence

📗 Chapter 1: Foundations of Language and Text Processing

🧠 Introduction

Before diving into machine learning and deep learning models in Natural Language Processing (NLP), it's crucial to understand the foundational concepts of language and how text is processed computationally.

Language is complex, nuanced, and deeply tied to context. Machines, on the other hand, operate in binary logic. Bridging the two is the purpose of NLP—and the first step in that journey is text processing.

This chapter introduces you to linguistic basics, text preprocessing techniques, and the importance of structured language analysis in preparing text for machine learning models.

📘 Section 1: Understanding Human Language Structure

Human languages have layers that contribute to meaning. These layers must be deconstructed and structured for machines to understand them.

🧩 Core Linguistic Components

Understanding these levels helps design better NLP pipelines, especially for tasks like part-of-speech tagging, parsing, and disambiguation.

📘 Section 2: The Need for Text Preprocessing

Raw text is noisy and full of ambiguities. Preprocessing ensures consistency, clarity, and a format that models can understand.

🔧 Common Preprocessing Tasks:

1. Tokenization: Breaking text into words or subwords
2. Lowercasing: Standardizing words
3. Stopword Removal: Removing common but non-informative words
4. Stemming: Reducing words to root form (e.g., “running” → “run”)
5. Lemmatization: More accurate root extraction using grammar rules
6. Punctuation & Noise Removal: Cleaning symbols, links, numbers, etc.
7. Normalization: Unifying slang, acronyms, or typos

🧪 Python Code: Basic Text Preprocessing with NLTK

python

import nltk

from nltk.tokenize import word_tokenize

from nltk.corpus import stopwords

from nltk.stem import PorterStemmer, WordNetLemmatizer

nltk.download('punkt')

nltk.download('stopwords')

nltk.download('wordnet')

text = "Cats are running quickly toward the big house, aren't they?"

# Tokenize

tokens = word_tokenize(text)

# Remove stopwords

filtered = [word for word in tokens if word.lower() not in stopwords.words('english')]

# Stemming

stemmer = PorterStemmer()

stemmed = [stemmer.stem(word) for word in filtered]

# Lemmatization

lemmatizer = WordNetLemmatizer()

lemmatized = [lemmatizer.lemmatize(word) for word in filtered]

print("Original:", tokens)

print("Filtered:", filtered)

print("Stemmed:", stemmed)

print("Lemmatized:", lemmatized)

📘 Section 3: Tokenization and Sentence Segmentation

📖 Tokenization

Tokenization breaks a string into individual components (words, subwords, or characters).

Tokenizers range from simple whitespace splitters to sophisticated Byte-Pair Encoding (BPE) and WordPiece used in models like BERT and GPT.

🧪 Code: Using spaCy Tokenizer

python

import spacy

nlp = spacy.load('en_core_web_sm')

doc = nlp("Don't panic. NLP's future is bright!")

tokens = [token.text for token in doc]

print(tokens)

📘 Section 4: Stopwords, Punctuation, and Cleaning

Stopwords are high-frequency, low-meaning words like “a”, “the”, “is”. Removing them helps focus the model on signal-bearing content.

Also, cleaning steps such as:

• Removing digits
• Handling contractions
• Removing special characters

are crucial to prepare high-quality input.

🧪 Code: Custom Preprocessing Function

python

import re

def clean_text(text):

    text = text.lower()

    text = re.sub(r"[^a-zA-Z\s]", '', text)

    return text

print(clean_text("Let's clean this TEXT!"))

📘 Section 5: Stemming vs Lemmatization

Stemming is faster but may produce non-words. Lemmatization is slower but more accurate and grammatically aware.

📘 Section 6: Text Representation

After preprocessing, the text must be numerically encoded for model input.

🔢 Common Techniques:

🧪 Code: TF-IDF with Scikit-learn

python

from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["I love NLP", "NLP is amazing and exciting"]

vectorizer = TfidfVectorizer()

X = vectorizer.fit_transform(texts)

print(vectorizer.get_feature_names_out())

print(X.toarray())

📘 Section 7: Importance of Language Corpora

A corpus is a structured dataset of real-world text used for:

• Training models
• Evaluating accuracy
• Learning linguistic patterns

Popular corpora:

• Brown Corpus (general English)
• Reuters (news articles)
• SQuAD (question answering)
• CoNLL-2003 (NER tasks)

✅ Chapter Summary Table