Advanced NLP

Advanced Natural Language Processing (NLP) is designed for students who aspire to pursue cutting-edge research in NLP. The course introduces core NLP tasks, including syntactic, semantic, discourse, and pragmatic analysis, along with the computational methods used to address these problems. Emphasis is placed on modern neural network–based approaches, covering fundamental modeling techniques and learning algorithms that underpin contemporary NLP systems. It also focuses on the theoretical foundations and practical methods required to build state-of-the-art language technologies.

Natural language data is central to modern AI systems and is being generated at an unprecedented scale across text, speech, and multimodal platforms. Making sense of this rich and complex data requires principled modeling, linguistic insight, and advanced learning techniques. Students study core NLP tasks such as syntactic, semantic, discourse, and pragmatic analysis, and explore contemporary neural approaches including representation learning, sequence modeling, attention mechanisms, transformers, and large language models. The course also covers probabilistic reasoning, learning from limited and noisy data, and evaluation methodologies, while situating NLP within the broader AI ecosystem.

Through hands-on assignments and a research-driven semester project, students analyze real-world language data, reproduce and extend recent research papers on an NLP topic of their choice, and gain experience with cutting-edge areas such as multilingual and low-resource NLP, generative AI, and ethical considerations in language technologies.

Natural language data is central to modern AI systems and is being generated at an unprecedented scale across text, speech, and multimodal platforms. Making sense of this rich and complex data requires principled modeling, linguistic insight, and advanced learning techniques. This course provides an in-depth treatment of Advanced Natural Language Processing, focusing on the theoretical foundations and practical methods required to build state-of-the-art language technologies. Students will study core NLP tasks such as syntactic, semantic, discourse, and pragmatic analysis, and explore contemporary neural approaches including representation learning, sequence modeling, attention mechanisms, transformers, and large language models. The course emphasizes probabilistic reasoning, learning from limited and noisy data, and evaluation methodologies, while situating NLP within the broader AI ecosystem. Through hands-on assignments and a research-driven semester project, students will analyze real-world language data, reproduce and extend recent research papers, and gain experience with cutting-edge topics such as multilingual and low-resource NLP, generative AI, and ethical considerations in language technologies.

By the end of this course, each student will have had the opportunity to: 
— Understand the linguistic, statistical, and neural foundations of Natural Language Processing, including syntax, semantics, discourse, and pragmatics.

1. Understand core modeling paradigms for NLP, such as probabilistic models, representation learning, sequence modeling, attention mechanisms, transformers, and large language models.
Apply advanced NLP algorithms and neural architectures to analyze and generate natural language from text and speech data.
2. Apply programmatic and mathematical methods to preprocess, model, and evaluate large-scale and noisy language datasets.
— Analyze challenges in multilingual, low-resource, and culturally grounded NLP, including bias, robustness, and generalization.
— Analyze and compare research methodologies, datasets, and evaluation metrics used in state-of-the-art NLP literature.
3. Synthesize complete NLP pipelines—from data collection and annotation to modeling, evaluation, and error analysis.
4. Design, implement, and evaluate an end-to-end NLP system or reproduce and extend a recent research paper using modern deep learning frameworks.

After completing this course, students should be able to

1. Learn the linguistic, statistical, and neural foundations of Natural Language Processing, including syntax, semantics, discourse, pragmatics, and modern NLP modeling paradigms such as probabilistic models, representation learning, transformers, and large language models | Know / Knowledge Outcome
2. Apply advanced NLP algorithms, neural architectures, and programmatic techniques to preprocess, analyze, and generate natural language from large-scale and noisy text and speech data | Apply Outcome
3. Analyze and critique multilingual, low-resource, and culturally grounded NLP methods, and evaluate state-of-the-art research methodologies, datasets, and evaluation metrics | Evaluate Outcome
4. Design, synthesize, and implement end-to-end NLP pipelines or reproduce and extend a recent research paper using modern deep learning frameworks, including data collection, modeling, evaluation, and error analysis | Create / Synthesize Outcome

1. Speech and Language Processing by Dan Jurafsky & James H Martin
2. Foundations of Statistical Natural Language Processing by Manning & Schütze

1. Hugging Face NLP Course — Free hands-on online course covering transformers, tokenizers, datasets, and fine-tuning (excellent complement to textbooks). ICYMI
2. ArXiv & Seminal Research Papers (e.g., Attention Is All You Need, BERT, GPT lines) — Essential for research-level understanding of modern architectures.

Online Lectures and Notes

1. Stanford’s CS224N (NLP with Deep Learning)
2. CMU’s CS11‑711 (Advanced NLP)
3. IIT/other universities’ advanced NLP course materials on GitHub, SWAYAM - NPTEL, etc. 
 — Often include slides, code, and assignments. Ncnynl

Practical Tools & Libraries (Useful for Labs/Projects)

1. spaCy — Industrial-grade NLP library in Python.
2. NLTK — Library + accompanying text for symbolic/statistical NLP.
3. Transformers (Hugging Face) — State-of-the-art pretrained models for research/project work.

[All Freshmore courses will have relative grading]

Note: Courses not having an exam will have an end semester jury for a maximum total of 40%.