Approaching (Almost) Any Natural Language Processing Problem
Contents:
- The obvious intro to NLP
- Bag of words
- TFIDF
- Hashing
- Cleaning text data (stemming, lemmatization)
- Tokenization of text data (from space-based to BPE and SentencePiece)
- Word embeddings
- Revisit LSTM, GRU, 1-D CNN, 2-D CNN
- Decomposition-SVD
- SVM, Logistic regression based models
- Topic modelling
- POS tagging, NER, entity detection/extraction using traditional approaches
- Attention is all you need
- Transformers
- BERT, RoBERTa, XLM, and other transformer based models
- Distillation of transformer based models
- Entity extraction using transformer based models
- Text summarization
- Various assignments and projects
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