- Understand approaches to syntax and semantics in NLP.
- Understand approaches to discourse, generation, dialogue and summarization within NLP .
- Understand methods of machine translation.
- Understand machine learning techniques used in NLP and its applications.
Bachelor in CS
Data will be available soon!
| content serial | Description |
|---|
| 1 | Introduction and fundamental algorithms |
| 2 | Applications Overview Machine translation, question and answering, chatbots and dialogue systems, automatic speech recognition, text to speech. |
| 3 | Estimation Techniques and Language Modeling (N-gram Models) |
| 4 | Naive Bayes and Sentiment Classification |
| 5 | Vector Semantics and Embeddings |
| 6 | Neural Networks doer NLP |
| 7 | Syntactic Structure and Dependency Parsing |
| 8 | Sequence labeling for parts of speech and named entities |
| 9 | RNNs and LSTMs models for NLP |
| 10 | Semantic Role Labeling and Argument Structure |
| 11 | Lexicons for Sentiment, Affect, and Connotation |
| 12 | Attention and Self-attention basics |
| 13 | Transformers and transfer learning for NLP |
| 14 | Machine Translation (MT) |
| 15 | Natural Language Generation/Summarization |
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