What are the methods of text processing in NLP from simple to modern neural networks?

Text processing methods in NLP can be conditionally divided into several stages and approaches, from simple to modern:

1. Text preprocessing: tokenization, normalization (converting to lowercase), stop-word removal, stemming, and lemmatization.

2. Bag of Words: representing text as word frequency counts without considering order.

3. TF-IDF: weighting words by importance in the document and corpus.

4. Word embeddings: models like Word2Vec, GloVe, FastText, which encode the semantics of words into vectors.

5. Classical ML models: based on features (e.g., SVM, logistic regression) for classification and information extraction tasks.

6. Recurrent neural networks (RNN), LSTM, GRU: consider the sequence of words, used for text generation, translation.

7. Transformers: modern models like BERT, GPT, which use attention mechanisms for effective context understanding.

8. Frameworks: libraries like TensorFlow, PyTorch, Hugging Face Transformers are used for implementation.

Thus, development progresses from simple statistical methods to deep learning using complex neural network architectures.