📜 Text representation is crucial in NLP as it converts raw text into numerical formats.

🧠 Essential for machine learning models that require structured inputs.

🔑 Text is unstructured, needing transformation for ML/DL models.

📈 Retains context and meaning for better semantics.

🚀 Enhances model performance by reducing noise.

📊 Traditional (Statistical) Approaches

🌐 Word Embedding-Based Approaches

🔍 Contextual Embeddings (Deep Learning-Based)

📈 BoW emphasizes word frequency while ignoring order and semantics.

🔍 E.g., "NLP is amazing" vs "Machine Learning is fun" as data corpus.

❌ Loses crucial word order and meaning.

📏 Leads to high dimensionality and sparse matrices.

🚫 Fails to account for semantic similarity among words.

🗣️ An N-gram is a contiguous sequence of N words for text analysis.

📚 Types include Unigrams, Bigrams, Trigrams, and Higher N-grams.

🔄 Helps improve context understanding in NLP.

🖥️ Essential for search engines and text prediction tasks.

📊 Enhances language modeling like Google Autocomplete.

📉 TF-IDF evaluates the importance of words within documents.

📊 TF determines word frequency, while IDF assesses rarity.

🔍 Useful for keyword extraction, search engine improvements, and text classification.

🔑 Affects document ranking based on term importance.

📦 Focuses on accessing necessary information based on user queries.

🌐 Google Search is a well-known example of an information retrieval system.

📈 Various models exist like Boolean, Vector Space Model, Probabilistic Models, and Neural IR Models.

🔗 Each type has unique methodologies for retrieving relevant documents based on user queries.

🔮 These models address uncertainties and rank documents based on relevance probability.

💡 Include Bernoulli and Binomial models as primary examples.

📖 Understanding text representation and information retrieval in NLP is essential for effective machine learning application.

🔑 Choosing the right techniques improves the accuracy of language processing tasks.