Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics, etc. In the last decade, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in NLP and students will also learn about how computational methods can help linguists explain language phenomena, including automatic discovery of different word senses and phrase structure.
Artificial Intelligence 132 CRs
Jurafsky, David, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition
| 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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