CS 173: Introduction to Natural Language Processing, Spring 2024 

Welcome to CS 173! 

1. We will use Piazza for questions and discussions in addition to office hours; unresolved queries by the TA will be escalated to the instructor. Class page at: https://piazza.com/ucr/spring2024/cs_173_001_24s/info

2. Direct emails/messages to the instructor should be limited to (1) medical or (2) grades issues.

3. For other questions and concerns, visit during office hours. Key announcements will be on eLearn/Canvas with email alerts.

Class Overview

Students will gain an overview of modern approaches for Natural Language Processing (NLP). Students will learn the theory and algorithms for NLP from applications such as part-of-speech tagging, parsing, named entity recognition, coreference resolution, sentiment analysis and machine translation.

Students who successfully complete this course will be able to:

1. Implement algorithms for basic language models

2. Install, configure and run sophisticated NLP toolkits

3. Identify which NLP tasks apply to given real world problems involving unstructured text data

4. Apply standard modelling techniques to a given NLP task

5. Research and apply current NLP modelling techniques to solve novel problems

Course Details

Prerequisites: CS 150

Format: The course consists of two 90-minute lectures and a one-hour discussion per week. Students are highly encouraged to utilize office hours for help on homework and exam review.

Instructor: Dr. Yue Dong — please communicate via eLearn or office hour, not email

Teaching Assistant:  Rishi Sangireddy <rishi.sangireddy@email.ucr.edu>

Lectures: TTh 9:30am - 10:50am, WCH room 143 

Discussion: W 11:00 - 11:50 am, Student Success Center | Room 121

Preliminary Schedule

Act 1: Preliminaries

Weeks 1-4:

• Introduction & Regular Expressions (ch 2)

• N-gram Language Models (ch 3)

• Naive Bayes (ch 4)

• Assignments (week 3)

• Midterm 1 (April 23rd, 2024)

Act 2: Modeling Techniques

Weeks 4-7: 

• Logistic Regression (ch 5)

• Vector Semantics (ch 6)

• Hidden Markov Models (Appendix A)

• Assignment (week 5,7,9)

• Midterm 2 (May 16th, 2024)

Act 3: Linguistics & Applications

Week 8-10: 

• Part of Speech Tagging (ch 8)

• Entity and Relation Extraction (ch 18)

• Question Answering, Information Retrieval (ch 14)

• Chatbots and Dialogue Systems (ch 15)

• FINAL EXAM

Homeworks and Tutorials

Study problems and the problems for the tutorials can be found on the Tutorials page.

You are expected to come to the tutorial class each week and make a good-faith attempt to work on the problems in your group.

Four assignments on eLearn will be due on Sunday evening (week 3,5,7, and 9).

Grading

• Homeworks — 30% — if you do well on homeworks, you will do well in this course

• Midterms (two) — 30% — if you complete homeworks successfully, you should excel on midterms

• Final — 20% — if you excel on midterms you should excel on final, which is comprehensive

• Quizzes, Attendance, Participation, Office Hours, and other activities — 20% — instructor's discretion

Standard +/- Scale: 90% or higher is the cutoff for an A- (similarly for B,C,D); 87% or higher is the cutoff for a B+ (and so on). 59% or less is an F. 

Textbook

Required Textbook: Dan Jurafsky and James Martin, Speech and Language Processing, 3rd ed (free) 

Additional Recommended References:

• Jurafsky & Martin, 3rd ed, draft is available online: web.stanford.edu/~jurafsky/slp3/

• C.D. Manning & H. Schuetze, Foundations of Statistical Natural Language Processing

Office hours

Instructor Office Hours: Tuesdays 11-12 noon, Thursdays 12 -1pm MRB 4135 

TA Office Hours: Mondays and Fridays over Zoom, Rishi Sangireddy,  https://ucr.zoom.us/j/8896652238

Additional Support: Academic Resources Center (ARC), 156 Surge, http://www.arc.ucr.edu