[Verse 1] Raw sentences cascade from digital streams Split them into tokens, break the seams Strip away the articles, the "and" and "the" Stop words vanish like autumn leaves Stemming chops the suffixes clean Running becomes run, the root machine Lemmatization goes deeper still Dictionary forms against your will [Chorus] Preprocess, represent, embed the meaning TF-IDF scores revealing Classical pipelines paved the neural road From bag-of-words to models that decode Token by token, feature by feature NLP foundations, every creature [Verse 2] Bag-of-words ignores the order's grace Counts frequencies, forgets their place TF-IDF weighs rarity high Common words get multiplied by nigh N-grams capture neighboring pairs Bigrams, trigrams climbing stairs Context emerges from the count Statistical mountains to surmount [Chorus] Preprocess, represent, embed the meaning TF-IDF scores revealing Classical pipelines paved the neural road From bag-of-words to models that decode Token by token, feature by feature NLP foundations, every creature [Bridge] Word2Vec learns from sliding windows wide Skip-gram and CBOW side by side GloVe factorizes global stats FastText handles subword formats King minus man plus woman equals queen Vector arithmetic, the hidden scene [Verse 3] Named entities tagged with IOB notation Parts-of-speech demand classification Dependency trees show grammatical links Sentiment analysis reveals what someone thinks BLEU scores measure translation quality ROUGE recalls the summary's honesty Perplexity predicts the next word's cost BERTScore shows what classical methods lost [Chorus] Preprocess, represent, embed the meaning TF-IDF scores revealing Classical pipelines paved the neural road From bag-of-words to models that decode Token by token, feature by feature NLP foundations, every creature [Outro] Build your pipeline, test and compare Classical methods with neural flair Foundation solid, the future's bright From sparse vectors to transformer might
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