CSCI-SHU 376: Natural Language Processing

Hua Shen

Course Agenda: 2026 Spring-NLP-[CSCI-SHU-376]-Class Schedule

2026-01-20

Spring 2026

Lecture 1: Introduction

Welcome to NLP course👏 !

Hello from Your Instructor!

Hua Shen

Assistant Professor of Computer Science

huashen@nyu.edu | huashen218

Research: Human-AI Alignment

Website: https://hua-shen.org/

• Office Hour: 2:30 PM - 4:00 PM, Friday; or By Appointment
• Office: S749; Zoom link: https://nyu.zoom.us/my/hua.shen (Passcode:enQb9h)
• Resources: Textbook, Syllabus Details
• Contact me: huashen@nyu.edu

Outline

1. What is Natural Language Processing (NLP)?
2. Course Overview and Logistics
3. Discussion: Know more about You 🧡 !

Outline

• What is Natural Language Processing (NLP)?
• Course Overview and Logistics
• Discussion: Know more about You 🧡 !

What is Natural Language Processing

What is NLP?

• Natural Language Processing:
• build program to automatically analyse, understand and generate human language in text
• Important branch of Artificial Intelligence
• NLP is an interdisciplinary field
• Healthcare, Law, Finance, etc

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What is NLP?

What is NLP?

• Play Diplomacy game with Human players!

What is NLP?

• 1950s–1980s: Rule-based NLP
• Hand-crafted rules, grammars, lexicons
• Tasks: parsing, MT, QA (ELIZA)
• Brittle, non-scalable
• 1990s–2000s: Statistical NLP
• Data-driven, probabilistic models
• N-grams, HMMs, PCFGs, MaxEnt
• Tasks: POS, NER, SMT
• Enabled by large corpora (Penn Treebank)
• 2000s–2013: Feature-Based ML NLP
• SVMs, CRFs, heavy feature engineering
• Sequence labeling, parsing, IE
• Limited transfer, costly features

NLP Landscape & History

• 2013–2017: Neural NLP
• Word embeddings (Word2Vec, GloVe)�RNNs, LSTMs, attention
• Seq2Seq models
• 2018–2020: Transformers & Pretraining
• Self-attention, pretrain–finetune
• BERT, GPT, T5
• Benchmark leaps (GLUE)
• 2021–Now: Foundation Models / LLMs
• Scale, in-context learning, RLHF
• Zero-/few-shot generalization
• New challenges: bias, hallucination, cost, safety

NLP Landscape & History

• Task Landscape
• Syntax → Semantics → Discourse
• Tagging, parsing, NER, QA, MT, summarization, dialogue
• Resources
• Data (Wikipedia, Common Crawl)
• Compute (GPUs/TPUs)
• Tooling (NLTK, spaCy, HuggingFace)
• Open Directions
• Multimodal NLP
• Low-resource languages
• Explainability & Controllability
• Human–AI collaboration
• Safety and Alignment

NLP Tasks & Directions

EMNLP 2025 Reference: https://2025.emnlp.org/calls/main_conference_papers/

Know More about State-Of-The-Art (SOTA) NLP

EMNLP 2025 Reference: https://2025.emnlp.org/calls/main_conference_papers/

Three top *CL conferences

• ACL
• EMNLP
• NAACL

NLP History 1: Statistical and Feature-Based NLP

https://medium.com/@antoine.louis/a-brief-history-of-natural-language-processing-part-1-ffbcb937ebce

Rule-based NLP

Rule Based NLP

• Rule based system, require careful programming
• Limited Domains

Statistical NLP

Statistical NLP

• Use machine learning approaches for NLP
• Statistical Machine Translation

https://medium.com/@antoine.louis/a-brief-history-of-natural-language-processing-part-1-ffbcb937ebce

NLP History 2: NLP with Deep Learning

NLP with Deep Leaning

• Significant Progress in NLP

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• Lots of Compute resources, large corpora

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• Little Feature Engineering

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NLP with Deep Leaning – Neural Machine Translation

NLP with LLMs

• Pre-trained on large corpus
• Fine-tuning / prompting on tasks
• One model for many tasks

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State-of-the-art LLMs

Language Model Hallucinates

Hallucination is an open question

• BingChat: Retrieval Augmentation

NLP Subfields

• Machine Translation
• Question Answering
• Information Extraction
• Text Summarization
• Syntactic Parsing
• Semantic Parsing
• ….

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Why NLP is (Still) Hard?

• Ambiguous
• Listener has to infer --- Pragmatics
• ….

Lexical Ambiguity

Syntactic Ambiguity

Semantic Ambiguity

Pragmatics

• Language conveys information about a user’s preference

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Outline

• What is Natural Language Processing (NLP)?
• Course Overview and Logistics
• Discussion: Know more about You 🧡 !

Logistics

• Lectures: Tuesday/Thursday 11:15am – 12:30pm, N401
• Office Hour: Friday 2:30-4:00pm (send me an email beforehand is preferred)
• Course Site: Brightspace
• Contains all information about the course (dates, contents, readings etc)
• Announcements will be posted on Brightspace as well
• Slides will be available before each class

Course Topics

• 2026Spring-NLP-[CSCI-SHU-376]-Class Schedule

Course Goal

• Understand basics of NLP (standard frameworks, algorithms etc)
• Know recent advances (e.g., LLM) and NLP applications
• Get hand-on experience, through assignments and final projects

Course Structure

Grading breakdown

• Attendance and Quizzes: 15%
• Homework Assignments: 15%
• Mid-term Exam: 25%
• [Project] Proposal: 5%
• [Project] Mid-term Presentation: 10%
• [Project] Final Report and Presentation: 30%

Course Structure

Quizzes (15%)

• There will be 7 quizzes, each quiz will focus on the previous topic we discussed in class (e.g., the first quiz will be about N-gram LMs).
• Each quiz will include one free-answer question (for 10-15 mins), about the key concepts in the corresponding topic.
• Quiz will be closed book.
• Your lowest quiz score will be removed.

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Course Structure

Assignments (15%)

1: LM, text classification, word embedding (5%)

2: sequence modelling (5%)

3: Transformer (5%)

• Each assignment consists of programming (in Python) and written components, and usually has 2 weeks
• You will have 48 free late hours for all assignments

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Course Structure

Midterm Exam (25%)

• True / False, Multiple-choice, free-answers
• You are allowed to bring a double sided A4 sheet
• Grades will be curved

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Course Structure

Final Project (45%)

Work on a specific problem with provided datasets and baselines. E.g., Agents, aiming for a research paper.

• A team project with (usually) <= 3 people per team
• Meet Hua at second week with your project ideas.
• [Project] Proposal (5%): your ideas and a related paper (5%)
• [Project] Mid-term Presentation (10%): relevant papers, and your progress / challenges (10%)
• [Project] Final Presentation (10%): your final project
• [Project] Final Report (20%): a conference style report
• Association for Computational Linguistics (ACL) conference Overleaf Template

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Final Project Team Registration

NLP-Spring-2026-Team Registration: https://docs.google.com/spreadsheets/d/1oRFu_igweQ7nY9lP50LkJypE2dE_llLD1oFo89uu_es/edit?usp=sharing

2026: Human + AI in Classs

Human-AI Collaboration is Encouraged!: Leverage AI as you need in this course

• Code editor + LLM

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2026: AI + Coding

• Operating system written by Cursor Only!!
• Do we still need software engineer ???

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2026: AI + Coding

2026: AI + Research

Van Noorden, R., & Perkel, J. M. (2023). AI and science: what 1,600 researchers think. Nature, 621(7980), 672-675.

Course Books

Textbooks

• The most popular textbook in NLP
• The book is frequently Updated

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Link: https://web.stanford.edu/~jurafsky/slp3/

Prerequisite

• We assume you have learned (or at least know a bit about) the following:
• Python
• Calculus, Probabilities and statistics
• Supervised Learning and. Unsupervised Learning
• Basics of Neural Networks

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Computing Resource

— Generative AI Tools and Services in NYU Shanghai

Outline

• What is Natural Language Processing (NLP)?
• Course Overview and Logistics
• Discussion: Know more about You 🧡 !

Love to know more about you!

What’s your experience + expectation on this course

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• Your name, major, year
• Why do you choose this course / What do you know about NLP?
• Do you have specific topics that you find interesting?

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Students Introduction