TextCraft Academy

Master NLP keyword extraction through 5 comprehensive chapters

5 Chapters • Interactive Demos • Hands-on Learning

Introduction to NLP

What is Natural Language Processing?

Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and generate human language. It bridges the gap between human communication and computer understanding.

🎯 Key Concept: NLP enables machines to read, understand, and derive meaning from human languages in a valuable way.

Common NLP Applications:

Sentiment Analysis: Understanding emotions in text
Machine Translation: Translating between languages
Chatbots: Automated conversation systems
Keyword Extraction: Identifying important terms (our focus!)
Text Summarization: Creating concise summaries

💡 Why Keyword Extraction?

Keyword extraction automatically identifies the most important words and phrases in a document. This is crucial for:

SEO optimization
Document indexing
Content summarization
Information retrieval

Text Preprocessing

Preparing text for analysis

Before extracting keywords, we need to clean and prepare the text. This process is called preprocessing and involves several steps.

The Preprocessing Pipeline:

Step 1: Tokenization
Breaking text into individual words or tokens.

                                        Input: "Hello World!"

                                        Output: ["Hello", "World"]

Step 2: Lowercase Conversion
Converting all text to lowercase for consistency.

                                        Input: ["Hello", "World"]

                                        Output: ["hello", "world"]

Step 3: Remove Stopwords
Filtering out common words that don't add meaning (a, the, is, etc.).

                                        Input: ["the", "cat", "is", "sleeping"]

                                        Output: ["cat", "sleeping"]

🧪 Try It Yourself

Term Frequency (TF)

The simplest keyword extraction method

Term Frequency (TF) measures how often a word appears in a document. The more frequently a word appears, the more important it might be.

TF(word) = (Number of times word appears) / (Total words in document)

Example Calculation:

                                    Text: "machine learning is great. machine learning is fun."

                                    Total words (after preprocessing): 6

                                    TF("machine") = 2 / 6 = 0.333

                                    TF("learning") = 2 / 6 = 0.333

                                    TF("great") = 1 / 6 = 0.167

                                    TF("fun") = 1 / 6 = 0.167

✅ Advantages:

Simple and fast to compute
Easy to understand
Works well for short documents

❌ Limitations:

Common words can dominate
Doesn't consider word importance
May not work well for long documents

🧪 TF Demo

TF-IDF Algorithm

Term Frequency - Inverse Document Frequency

TF-IDF improves upon TF by considering how rare or common a word is across different parts of the document. It balances frequency with uniqueness.

TF-IDF = TF × IDF
where IDF = log(Total Sections / Sections containing word)

Understanding IDF (Inverse Document Frequency):

IDF measures how rare a word is. Words that appear in every section get low IDF scores, while rare words get high IDF scores.

                                    Example with 3 sentences:

                                    1. "machine learning algorithms"

                                    2. "deep learning networks"

                                    3. "machine vision systems"

                                    IDF("learning") = log(3/2) = 0.176 (appears in 2/3 sentences)

                                    IDF("machine") = log(3/2) = 0.176 (appears in 2/3 sentences)

                                    IDF("algorithms") = log(3/1) = 0.477 (appears in 1/3 sentences - more unique!)

🎯 Why TF-IDF is Better:

Balances word frequency with uniqueness
Reduces impact of common words
Highlights truly important terms
Industry standard for keyword extraction

🧪 TF-IDF Demo

RAKE Algorithm

Rapid Automatic Keyword Extraction

RAKE is different from TF and TF-IDF because it extracts key phrases (multiple words) instead of just single words. It uses word co-occurrence patterns.

How RAKE Works:

Step 1: Split by Stopwords
Text is split wherever a stopword appears, creating candidate phrases.

                                        "The machine learning algorithm is powerful"

                                        → Candidates: ["machine learning algorithm", "powerful"]

Step 2: Calculate Word Scores
Score = (Word Degree + Word Frequency) / Word Frequency
Degree = how many words it co-occurs with

Step 3: Calculate Phrase Scores
Sum the scores of all words in the phrase.

RAKE Score = Σ (individual word scores in phrase)

🌟 RAKE Advantages:

Extracts multi-word phrases
Domain independent
No training data required
Great for technical documents

Best Used For:

Scientific papers
Technical documentation
Academic articles

🧪 RAKE Demo

Your Progress

Chapter 1: Introduction to NLP

Chapter 2: Text Preprocessing

Chapter 3: Term Frequency

Chapter 4: TF-IDF Algorithm

Chapter 5: RAKE Algorithm

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