- 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 of Computer Science - 132 CRs
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 |
Start your application