Natural Language Processing

NLP Fundamentals

NLP processes and analyzes text data.

Text Preprocessing

Tokenization: split text into words/tokens. Lowercasing, removing punctuation.

Stop words removal, stemming/lemmatization reduce vocabulary.

Word Embeddings

Word2Vec creates dense vector representations. GloVe pre-trained embeddings.

CountVectorizer, TfidfVectorizer create bag-of-words representations.

Text Classification

Naive Bayes: text classification classic. Logistic regression on TF-IDF works well.

LSTM, BERT for deep learning approaches.

Key Takeaways

1. Preprocessing is crucial for NLP
2. TF-IDF provides simple text representation
3. Deep learning (BERT) provides state-of-art